Real-time recognition of cattle using animal biometrics - Shrikant … · 2018. 7. 4. · Shrikant...

SPECIAL ISSUE PAPER

Real-time recognition of cattle using animal biometrics

Santosh Kumar1 • Sanjay Kumar Singh1 • Ravi Shankar Singh1 •

Amit Kumar Singh2 • Shrikant Tiwari3

Received: 14 June 2016 / Accepted: 3 October 2016

� Springer-Verlag Berlin Heidelberg 2016

Abstract With the advent of efficient recognition tech-

niques, animal biometric systems have gained more prolif-

eration for the identification and monitoring of cattle. A

cattle biometric system is a pattern recognition-based system

for the identification of livestock. In this paper, we propose a

novel muzzle point recognition based on Fisher locality

preserving projection algorithm for the recognition of cattle

in real time. We have captured images of animals using a

surveillance camera and transferred them to the server by

wireless network technology. The major contributions are as

follows: (1) preparation of muzzle point database, (2)

extraction of the salient set of features using proposed

muzzle point recognition approach, and (3) evaluation and

comparison analysis of the introduced method and several

existing recognition algorithms on a standard benchmark

protocol. The efficacy of proposed muzzle point recognition

approach for cattle evaluates under identification settings

and yields 96:87% recognition accuracy for identifying

individual cattle. The proposed approach also valued the

10.25 sec recognition time for enrollment and identified

individual cattle on different sizes of muzzle point images.

Keywords Real-time image processing � Animal

biometrics � Muzzle point � Cattle recognition � FLPP �FLDA � SVM � Feature extraction � Classification

1 Introduction

Animal recognition systems have received much attention

and proliferation due to the wide range of applications and

used in the field of animal biometrics, computer vision,

pattern recognition, and cognitive science [34]. The cattle

recognition system can apply to the variety of applications,

such as animal registration, traceability, monitoring, iden-

tification of missed, swapped of cattle, reallocation of

livestock, and verification of false insurance claims

[14, 35]. However, classical animal recognition-based

methodologies include the manual approaches, such as ear-

tagging, ear tips or notches, freeze-branding, and embed-

ded RFID-microchip to recognize the individual cattle [3].

On the other hand, automatic recognition and behavior

analysis of animal can be done by captured images of ani-

mals [11]. Due to the enormous amount of man-power

requirements, classical animal recognition approaches have

high cost and venerability loses due to duplication, fraudu-

lent, and forged of embedded standard tags. The human

interpretation is subjective in prestigious animal recognition

approaches [3]. The registration process of the animal pro-

vides techniques to control the various kinds of efforts for

& Santosh Kumar

[email protected]

Sanjay Kumar Singh

[email protected]

Ravi Shankar Singh

[email protected]

Amit Kumar Singh

[email protected]

Shrikant Tiwari

[email protected]

1 Department of Computer Science and Engineering, Indian

Institute of Technology (Banaras Hindu University),

Varanasi 221005, India

2 Department of Computer Science and Engineering, Jaypee

University of Information Technology, Solan,

Himachal Pradesh 173234, India

3 Department of Computer Science and Engineering, Shri

Shankaracharya Technical Campus, Junwani,

Bhilai, District-Durg, Chattisgarh 490020, India

123

J Real-Time Image Proc

DOI 10.1007/s11554-016-0645-4

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the manipulation and swapping of cattle. The traceability

process provides a safer food supply. It also provides the

excellent platform for producers and consumers [32].

Moreover, traceability process also caters a procedure to

verify the owner or parentage of animals. It also controls and

outbreaks the critical diseases of animals for their health

management in the livestock [63].

The registration of cattle is one of the essential com-

ponents in the emerging animal biometrics, livestock

framework-based systems, and classical animal recogni-

tion-based approaches.

The classical animal recognition methodologies are ear

tags, freeze-branding, ear tattoos, ear tips or notches, and

embedding of microchips in the animal body. These are

invasive approaches for the recognition of individual cattle.

However, classical animal recognition-based approaches are

more susceptible to massive vulnerability to loss and illeg-

ibility. The classical animal recognition techniques always

lead more security issues for the protection of cattle or other

animals [29]. The real-time cattle recognition system pro-

vides an efficient way to stop the cattle manipulations. In the

classical animal recognition systems, probability of regis-

tration and identification of animal using ear tags is more.

However, ear tags became damaged, lost, or stolen easily.

The duplication and forge of the labeled unique number in

the ear tags done easily. Therefore, it has more vulnerability

due to losses and major security issue-related ear-tagging

system for identification of different cattle.

In the classical animal recognition systems, there is

more probability of registration and recognition of animal

using ear tags. However, ear tags became damaged, lost, or

stolen easily. The unique number in the ear tags can be

duplicated, forged. It has more vulnerability due to losses

and major security issue-related ear-tagging system.

In case of false insurance claim verification of animals,

there is no such real-time animal biometric-based recog-

nition systems that are available in the literature or public

domain to cater a better protection, verification, and

recognition of individual cattle or livestock (animal) using

their primary animal biometric characteristics.

In current state-of-the-art-based animal recognition

approaches, the animals are verified and identified to solve

the biggest problems of the false insurance claim by cutting

their ear or snatching the labeled ear tags or notches from

the animals ear by different governmental organizations

and private animal insurance providers. It is evident that a

falsification in the labeled ear tags can make duplication,

forgery, and fraudulent process. Therefore, it is tough to

verify and recognize the registered insurance animals

(owner of cattle) or impostor (non-insurance) animal.

In classical animal recognition-based systems, various

emerging technologies have been investigated to provide a

better solution to identification, monitoring, and tracking

problems of cattle using GPS positioning, Bluetooth or Wi-

Fi networks, and RFID Huhtala et al. [25]. However, it

reported major problems during the practical implementa-

tion for embedding the sensors in the animal’s body. The

emerging technologies are unable to provide better security

to livestock or cattle. Moreover, these techniques are

invasive and have the significant problems for embedding

the electronic devices in their body [56].

Besides that, all categories of classical animal recogni-

tion approach, the artificial marking-based techniques can

be duplicated, fraudulent, and forged the embedded a

unique identification number of ear-tags. Therefore, it is

unable to recognize and verify the false insurance claims.

Due to the significant limitations and failures of classical

animal recognition-based approaches, there is the need to

design and develop a real-time cattle recognition system

for identification, verification, and monitoring of cattle,

individual animals and other livestock.

1.1 Motivation of work

The identification of animals is the major problem for verifi-

cation of false insurance claims, animal tracking, animal

monitoring, identification of locomotion and posture behavior

of livestock animal or cattle before calving in the livestock

framework, and prestigious animal recognition-based systems.

In our acknowledgment, there is no such animal bio-

metrics-based cattle recognition systems available in the

public domain. To provide better solutions for identifica-

tion and verification of false insurance claims, monitoring

of livestock and assistance during health management of

animals, and efficient recognition are required for the

prevention of critical diseases, food, feed, and distribution

of cattle in the livestock framework.

These are the major problems of identification and

monitoring of animal in the classical animal recognition

approaches and traditional livestock framework based

systems. These problems can not be ignored by various

scientists, veterinary professionals, animal experts, and

different research communities to contribute valuable

efforts for the design and development of robust, nonin-

vasive, and real-time animal biometric-based recognition

systems for identifying individual cattle. Therefore, it is

required to develop a real-time cattle recognition system

for identifying and monitoring of different animals.

In this paper, we propose a muzzle point recognition-based

cattle recognition system for identifying individual animals

(especially cattle) in the real-time. The images of cattle are

captured using surveillance camera. The surveillance cameras

are deployed in the stables of cattle. The total number of cattle

is 500 in the stable of theDepartment of Dairy andHusbandry,

Institute of Agriculture Science (I.A.S), Bananas Hindu

University (B.H.U), Varanasi-221005.


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The proposed cattle recognition system provides better

solution to recognition and monitoring of cattle or livestock

animals. These recognition of cattle using classical animal

identification approaches include only invasive techniques.

For example, embedded microchips or attachments (e.g.,

chips, RFID, transponders, ear-tagging, ear tip or notches,

freeze-branding, and hot ironing) are invasive-based recog-

nition techniques. The benefits of the proposed cattle

recognition system are cost-effective, non-invasive, auto-

matic, easy to acquire, accurate and also humane.

In this paper, a muzzle point recognition-based solution

gives the automatic recognition of cattle in the real time.

To address this problem, we use surveillance cameras for

the automatic capturing of images of cattle for identifica-

tion purpose in the stables (farm house) of cattle.

For the recognition of cattle, surveillance cameras have

been deployed in the stables of Dairy and husbandry,

Institute of Agriculture Sciences (I.A.S) Bananas Hindu

University (B.H.U), Varanasi-221005. The surveillance

cameras continuously record and capture the images of

cattle. The proposed real-time cattle recognition system

extracted the video frames (i.e., the set of pictures of cattle

muzzle points pattern) from the captured surveillance video

and used as the input image for the cattle recognition

system.

We use wireless networks (i.e., Internet technology) to

transfer the image of animals (cows) to the server of the

cattle recognition system (shown in Fig. 1). The server

creates a database of cattle images. Figure 1 illustrates the

transferring of cattle images and recognition of cattle.

The muzzle point images of cattle are cropped from the

extracted video frames [set of video frames (image)]. After

that image enhancement techniques are applied to mitigate

the noises and improve the contrast of muzzle point ima-

ges. However, the surveillance camera captures the video

from the unconstrained environment (e.g., low illumina-

tion, poor image quality, head movement of cattle, and

blurred). Moreover, muzzle point images are segmented to

find the discriminatory region of interest to extract the

features from segmented muzzle point images.

After that muzzle features are obtained from the muzzle

point image database, using proposed Fisher locality pre-

serving projections (FLPP) technique, texture feature

descriptor algorithms, and appearance-based feature

extraction algorithms. The proposed system selects the sets

of discriminatory features from the extracted muzzle point

features. Then, unique templates of muzzle point image are

generated from the selected set of features and stored in the

muzzle point image database (shown in Fig. 1).

Fig. 1 Illustration of transferring the images and recognition of cattle


123

In the testing phase, a query (test) muzzle image of

cattle is matched with stored muzzle point template data-

base using one-shot similarity (OSS), distance matrices-

based matching technique, and first-class SVM classifica-

tion model for the recognition of cattle in the real time

(shown in Fig. 1).

The advantages of the proposed cattle recognition sys-

tem are low cost, invasive, automatic recognition, and

monitoring of animals in real-time scenario. The proposed

real-time cattle recognition system does not need any

expensive and noninvasive-based extra hardware, such as

RFID-tags, embedded microchips, hot ironing, freeze-

branding, sensors, and the local database to recognize

individual cattle.

The proposed recognition system provides a non-contact

solution, cost-effective, and robust recognition of livestock;

thus, there is no requirement tomount or embed any invasive

artificial marking hardware identifiers to the cattle.

1.2 Major contributions of work

The major contributions of this research work are as

follows:

1. The unique physiological characteristics and un-coop-

erative behaviors of animal (especially cattle) achieve

new leads to interesting, significant challenges for

cattle recognition using the animal biometric system in

real time. Based on the consideration of non-intrusive

characteristics of animal biometric systems, this

research work explores the new possibility of cattle

recognition using muzzle point pattern recognition

algorithms for determining the identity of cattle based

on their primary biometric characteristics, such as

muzzle point image pattern of individual cattle.

2. In this paper, we propose the Fisher locality preserving

projections approach for the recognition of cattle using

muzzle point images in the real time and demonstrates

the current state-of-the-art results.

3. The proposed real-time cattle recognition system

utilizes Fisher locality preservation technique for the

recognition of individual cattle in the real time. The

Fisher locality preserving projections are a new graph-

based muzzle point feature representation approach in

linear feature subspace that maximizes the inter-class

(between-class) scatter of muzzle point image feature

matrix and effectively minimize the intra-class

(within-class) scatter matrix of muzzle point images

to improve the recognition accuracy.

4. We introduce a simple segmentation technique to find

the ROIs of muzzle point image pattern for the better

segmentation of only required the most discriminatory

and informative area of muzzle point images.

5. The proposed Fisher locality preserving projection

approach extracts the features of muzzle point images

of cattle. By applying this approach, only the most

discriminant and stable muzzle features are preserved

for the representation and recognition of individual

cattle.

6. The database ofmuzzle point image pattern of 500 cattle

(subjects) is prepared with surveillance cameras using

60–120 mm lens from the Department of Dairy and

Husbandry, Institute of Agriculture Sciences (I. A. S.),

Bananas Hindu University (B.H.U.), Varanasi, India-

221005. The size of muzzle image database is 5000 (i.e.,

500 subject’s �10 images of each subject). The size of

each muzzle point image is 200� 200 pixels.

The rest of the paper is organized as follows: Sect. 2

reviews the related works in the field of cattle recognition,

tracking, and monitoring in the real-time scenario. Sec-

tion 3 illustrates the preparation and description of muzzle

point image database for recognition of cattle. Section 4

presents the proposed recognition system for cattle recog-

nition using their muzzle point images. Section 5 shows

brief descriptions of feature extraction and matching using

proposed Fisher locality preserving projections (FLPP) for

feature representation. Section 6 illustrates the experi-

mental results along with their detail performance evalua-

tion and analysis. Finally, Sect. 7 is dedicated to the

conclusion and future directions.

2 Related work

The animal biometric-based recognition systems have

received much attention in recent years for recognition,

tracking, and monitoring of different species or individual

animal. The recognition systems recognize the animal

using their physiological characteristics (e.g., facial ima-

ges, morphological pattern) and visual features of their

body [9, 17, 20]. However, the prestigious animal recog-

nition-based systems recognize individual using identifi-

cation methodologies [33]. The classical animal

recognition methodologies can be divided into several

groups, namely (1) permanent recognition methods, (2)

semipermanent recognition methods, and (3) temporary

recognition methods [35]. The classification of classical

animal recognition approaches is shown in Fig. 2.

The permanent animal recognition methodologies pro-

vide the animal protection by recognizing animal using ear

tattoos, ear-tip or notches, freeze-branding (hot iron around

animals neck), and embedded microchips or transponders

in the electronic devices, sensors, and RFID [33].

For example, ear-tagging, ID-collar-based recognition

techniques are semipermanent-based recognition


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approaches for the identification of individual cattle.

However, the embedded label of ear tags can be easily lost,

duplicated, and forged. The ear-tagging systems include

various kinds of metal clips and plastic tags. However,

these ear tags can cater different types of infections or

critical diseases to animals after the embedded of these tags

into animal body

For example, ear-tagging, ID-collar-based recognition

techniques are semipermanent-based recognition approaches

for the recognition of individual cattle. However, the

embedded label of ear tags can be easily lost, duplicated, and

forged. The ear-tagging systems include various kinds of

metal clips and plastic tags. However, these ear tags can

cater various kinds of infections or critical diseases to ani-

mals after the embedded of these tags into animal body [3].

The ear-tagging and ID-collar-based cattle recognition

approaches are susceptible to damage, duplication, losses,

un-readable, and fraud of ear tags; therefore, these

approaches do not perform well for identification of indi-

vidual livestock cattle in the long term [59].

While in temporary recognition-based methodologies, it

recognizes the individual animal by applying the sketch

pattern-based techniques. For example, paint or dying and

RFID-based animal recognition with embedded transpon-

ders or sensors in their body are temporary identification

methods [29].

Among these available techniques, the RFID-based

animal identification and tracking process is one of the

most promising for the cattle or other livestock animals.

The RFID-based animal recognition systems operate in two

types of scenarios (1) active RFID-based animal identifi-

cation and tracking technique and (2) passive RFID-based

animal identification and tracking based technology. The

active RFID equipment consists of a temperature sensor,

the power source (battery backup) and microchips. It is also

known as RFID transponder. The active tags can broadcast

the signal similar to a cell phone. Therefore, it sends the

alert message or signal to registered animal owners or farm

house owner when livestock animal or cattle becomes sick.

Moreover, it provides a way to easily track the health and

movement of the livestock animal in the herd. It also

provides a way to follow the animal in the real-time.

On the other hand, passive RFID-based technique utilizes

the various equipment and sensor devices. The owner or

parentages of livestock animalsmount these devices in the ear

of different cattle. RFID readers are deployed in a lot of dif-

ferent places for reading RFID unique number in the form

house or stables of animals [56]. However, RFID-based ani-

mal identification framework requires a significant number of

RFID readers, scanner, or various antennas for the proper

communications. Therefore RFID and sensor devices (GPS

systems, embedded wireless microchips) based animal iden-

tification and tracking approaches are not a cost-effective

solution for the cattle recognition and monitoring [46]

All classical animal recognition approaches are invasive,

expensive and also vulnerable to losses of ear tags [31].

However, the performance of conventional animal recogni-

tion methods is tiny, limited, due to their vulnerability to

losses, easily duplication, fraud, major security issues, and

challenges [3]. Therefore, classical animal recognition

Fig. 2 Classification of classical animal recognition approaches


123

approaches are unable to provide a competent level of

monitoring and protection to livestock animals [51].

The research focus has shifted and advancement has

provided a new paradigm for the control, tracking, and

recognition of human or animals using surveillance camera

and intelligence devices [47] in the farm houses and live-

stock frameworks [16, 22, 65].

In the available literature, different models and frame-

works are found to solve the problems of monitoring,

tracking, recognition, and analysis of animal behavior.

Martiskainen et al. [44] proposed a system using computer

vision techniques for the monitoring and tracking of cattle

in the farm houses. The proposed system provides the

tracking information of livestock and localizes the loco-

motion of postures of animals [11, 21, 52].

For example, various animal tracking and monitoring

techniques, such as global positioning system (GPS), Wi-

Fi, wireless networks, RFID, and Bluetooth-based moni-

toring and tracking methods cater a better platform for

tracking and monitoring of cattle [2, 25]. However,

deployments of these techniques are not suitable in practice

and more difficulties in attaching or embedding any elec-

tronic monitoring devices to cattle body. The classical

animal recognition and surveillance identification approa-

ches provide only evaluation and analysis, based on manual

scoring techniques for the quantification of animal behav-

ior in the field or animal farmhouses [48].

The similarity matching-based techniques can apply for

the analysis of animal behavior using videos. These

methods consume more time and require considerable

man-hour resources. Therefore, these techniques have

consumed more preprocessing and feature extraction time

for computing the feature vectors and predicting the correct

analysis of features. To provide the better improvement in

the behavior analysis, expert as human interpretation is also

required as individual support [41].

To solve these major problems various intelligence

devices, sensors, and chip-based devices have been

deployed by the embedding of these sensors and devices to

the animals body for the quantification and analysis of

animal behaviors. However, these methods are also inva-

sive for animals and alter the behavior of the animal [26].

The computer vision, machine learning, and image

processing techniques have achieved recently more atten-

tion for recognition and monitoring of cattle in the dairy

industry [12, 42].

The author [28] proposed a framework for the investi-

gation of locomotion of dairy cows using high-speed cin-

ematography techniques [18]. The identification

framework and computational models are employed in the

variety of applications for cattle recognition such as

tracking of livestock [43].

The developed real-time-based framework system

gathers the enormous amount of information by capturing

the body variations in different poses from the top image

sequence (video frames) of different surveillance video of

pregnant cows in the real-time environment. The major

shortcoming is that it does not validate the experimental

results during the classification of behavioral activities of

pregnant cattle before calving.

In the direction of animal tracking, different tracking

algorithms are developed [38]. These tracking approaches

offered more flexibility in the monitoring of large-sized

animals in a constantly changing background. Author [37]

proposed an animal recognition model using computer

vision system to study the behavior of hens in furnished

cages. Individual behavior, such as standing, walking, and

scratching, could be recognized automatically in real-time

scenario [10].

Cangar et al. [10] proposed a framework for the moni-

toring and tracking of locomotion and posture, the behavior

of pregnant cows, before calving, using computer vision-

based approaches in real time.

In the similar direction, health monitoring of poultry, the

author proposed a framework based system using computer

vision approaches. They applied various surveillance

cameras for the diagnosis of critical diseases and moni-

toring of poultry flock health [37]. Moreover, the authors

proposed an animal recognition model using computer

vision system to study the behavior of hens in furnished

cages. Individual behavior, such as standing, walking, and

scratching, could be recognized automatically in real-time

scenario.

Shao et al. [55] proposed a method to identify and

analyze swine behaviors using the programmable cameras.

The two parameters, such as area and the perimeter of the

top views, were applied to detect the swine from the cap-

tured image database.

In the same direction, Tillett et al. [57] used the image

processing and computer vision-based techniques for the

recognition and analysis of the behavior of pigs. The pigs

were identified in surveillance video by tracking and

detecting their movements.

Recently, in commercial livestock houses, image analysis

for behavior classification becomes more complicated due to

Table 1 Details of the muzzle point image pattern database

Breeds (races) No. of subjects (cattle) No. of images

Balinese cow 150 1500

Hybrid Ongole cow 150 1500

Holstein Friesian cow 100 1000

Crossbreed cow 100 1000


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lighting condition and dynamic changes. But camera char-

acteristics, background, and test subjects characteristics all

these things influence the ability of the system to recognize

the issue and record its movement accurately [24].

3 Database preparation and description

The database of muzzle point image is prepared with

surveillance cameras using 60–120 mm lens from the

Department of Dairy and Husbandry, Institute of Agriculture

Sciences (I. A. S.), Bananas Hindu University (B.H.U.),

Varanasi, India-221005. The size of muzzle point image

database is 5000 (500 subject �10 images of each subject).

The size of each muzzle point image is 200� 200 pixels.

The detail description of the database is given in Table 1

and Fig. 3 illustrates some muzzle point image pattern of

cattle.

3.1 Characteristics of muzzle point image pattern

of cattle

In our affirmation, real-time cattle recognition system using

muzzle point image pattern can be more friendly, noninva-

sive, and cost-effective for identification, verification, moni-

toring, and tracking of individual animals, if the performance

of automatic matching algorithms is more satisfactory.

The muzzle point image is a unique and stable discrim-

inatory biometric characteristic of cattle for the recognition

purpose. The recognition of muzzle point image pattern is

similar to the recognition of minutiae points in the human

fingerprint [3, 27, 34].

The discriminatory biometric pattern of muzzle point

images can be clustered into two significant patterns known

as beads and ridge pattern. The beads pattern of muzzle

point image consists of irregular structures, and their shape

is similar to the islands, whereas ridges pattern is similar to

rivers structure. The ridge pattern always separates the

beads. The beads and ridges of muzzle point are unique

biometric identifiers for the recognition of individual cattle.

The research in the field of cattle recognition using their

muzzle point images characteristics is limited by the non-

availability of a large muzzle point image database for the

better research and experimentation purpose. However, few

researchers have proposed some cattle recognition

approaches to identify individual cattle based on only their

muzzle (nose) print image databases of cattle.

The beads pattern of muzzle point image consists of

irregular structures, and their shape is similar to the islands,

whereas ridges pattern is similar to rivers structure. The

ridge pattern always separates the beads. The beads and

ridges of muzzle point are unique biometric identifiers for

the recognition of individual cattle. Therefore, these animal

recognition systems are not available in the public domain

for the recognition and verification of cattle.

4 Proposed cattle recognition system

In this section, the proposed real-time cattle recognition

system using muzzle point images is illustrated in detail.

For the manifestation of the objective of the proposed cattle

recognition system, the surveillance cameras are deployed

in the cattle stables of Department of Dairy and Husbandry

for continuously monitoring through capturing the animals

(especially cattle) video.

The proposed real-time cattle recognition system con-

sists of two phases: (1) training phase and (2) testing phase.

In the training phase, the cattle recognition system builds a

database of the muzzle point images from the surveillance.

The proposed system extracts the video frames (i.e., a set

of images) from the captured surveillance video.

The obtained cattle images are applied as input images

to cattle recognition system. The muzzle point images are

cropped from the extracted video frames. The image

enhancement techniques are applied to mitigate the noises

and improve the contrast of muzzle point images because

the video is captured from the unconstrained environment

(e.g., low illumination, poor image quality, head movement

of cattle, and blurred).

The muzzle point images are preprocessed to mitigate

the noises, and images are converted into grayscale muzzle

images. After that, muzzle point images are segmented to

find the discriminatory region of interest to extract the

features from segmented images.

After that, muzzle features (e.g., texture features and

pixel intensity values of muzzle point images) are extracted

from the muzzle point image database using texture feature

descriptor algorithms, appearance-based feature extraction

algorithms. The discriminatory muzzle features are selec-

ted from the extracted features. The unique templates of

muzzle point image are generated from the selected set of

features. Then, muzzle image templates are generated and

stored in the muzzle point database.

In the testing phase, recognition system takes the input

muzzle image as a test (query) image. The muzzle point

Fig. 3 Some muzzle point image pattern of cattle from database


123

features are extracted using proposed graph-based feature

extraction technique known as Fisher locality preserving

projections (FLPP).

The FLLP feature extraction and representation tech-

nique utilizes two feature graphs with the first graph uti-

lized to distinguish the compactness of within-class SWð Þscatter matrix of muzzle point image database. The second

graph is assigned and employed to the augmentation of

scatter matrix feature separability of the between-class SBð Þin the muzzle point image database.

The primary evidence to apply the proposed FLPP

approach is to preserve only the most discriminant and

immutable muzzle point image features retained. Finally, the

feature vectors of muzzle point images are achieved and

matched with their counterpart in the stored muzzle point

templates databases. After that, obtained similarity scores of

Fig. 4 Proposed block diagram of real-time cattle recognition system

Fig. 5 Illustration of pattern of muzzle point image for the calf

registration


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muzzle point images are compared with defined threshold

values hð Þ to produce a final decision for cattle recognition.

The proposed real-time cattle recognition system is

shown in Fig. 4. The steps involved in the real-time cattle

recognition are given in brief as follows:

Figures 5 and 6 illustrate beads and the ridge pattern of

muzzle point images. The bead and ridges are extracted for the

recognition of cattle using texture descriptor-based recogni-

tion techniques and appearance feature extraction algorithms.

4.1 Preprocessing of muzzle point images

In the preprocessing steps, preprocessing techniques are

applied to mitigate the specific degradation, such as noises

from captured muzzle point images. The muzzle point

images are from captured video which are taken from

unconstrained environments (i.e., poor illumination, pose,

and blurring of pictures) using surveillance video. There-

fore, it may be defective and deficient in some respect, such

as poor image quality, low contrast, and blurred. Some

sample images of muzzle point are shown in Fig. 7. The

preprocessing steps are essential for feature extraction,

segmentation, feature representations, and matching.

The segmentation technique is applied to find the ROI

from the original muzzle point images and provides

enhancement of the recognition accuracy of cattle. How-

ever, the ROI of muzzle images must be chosen carefully

to cater all the discriminatory features of muzzle point

images. The preprocessing step is shown in Fig. 8.

We have applied a CLAHE image enhancement technique

to improve the contrast of low quality of muzzle point images

[50]. Enhancing themuzzle image contrast helps to reduce the

illumination effect and boost the fine patterns of beads and

rides as main discriminatory features are shown in Fig. 8.

Figure 8 illustrates the pre-processing of beads and the ridge

pattern of original muzzle point images. In Fig. 8, (a) shows

the input muzzle point image, (b) illustrates the blurred

muzzle point image pattern, (c) demonstrates the pre-pro-

cessing and extraction steps for the beads and the ridge pattern

ofmuzzle point images, and (d) shows the filtrating process of

overlapped regions between beads and ridge pattern of

muzzle point images. After the enhancement of the low

illumination, poor image quality, and blurred muzzle images,

we have applied the segmentation techniques to partition the

muzzle point images into different image segments to find out

the region of interest. After that, segments of muzzle point

images are selected to locate the region of interest (ROI) in the

muzzle point images. Each region of interests of muzzle point

images is determined for the extraction of discriminatory

features (themost informative pattern of beads and ridge area)

of muzzle point images.

Fig. 6 Beads and ridges of the

muzzle point pattern of cattle

from database

Fig. 7 Some muzzle point image of cattle has problem of poor

illumination (1, 3, and 6) and blurred muzzle point images (2, 4, and

5) during database acquisition


123

4.2 Segmentation of muzzle point images

In this section, a simple muzzle image-based ROIs seg-

mentation algorithm for muzzle point image is illustrated in

brief. The segmentation algorithm performs the segmen-

tation of muzzle point images into the number of ROIs

muzzle bead and the ridge pattern for the extraction of only

the most discriminatory and more informative feature from

these segmented RIO areas.

After the enhancement of muzzle images, segmentation

algorithms are applied to find ROIs from the segmented

muzzle point images. We have applied color K-means

clustering and texture-based segmentation algorithms. The

segmented muzzle point images using color K-means

clustering and texture-based segmentation technique are

shown in Figs. 9, 10, and 11, respectively.

Figure 11 illustrates the segmentation of muzzle point

image, where ground truth images are shown in (b–e) to

find the ROIs from the segmented original muzzle

image, where (a) shows the segmentation of muzzle

images using texture segmentation algorithm. The muz-

zle point images contain discriminatory color features.

Therefore, a K-means color-based segmentation tech-

nique is applied to segment the texture feature into

different clusters, where (a) illustrates the segmentation

of muzzle images using texture segmentation algorithm

Fig. 8 Preprocessing of beads

and ridges from the original

images of muzzle point pattern

Fig. 9 a Input muzzle point

images b segmented of muzzle

point pattern using a K-means

clustering segmentation

algorithm


123

and (b) segmented muzzle point pattern, shown in

Fig. 10.

4.3 Learning-based encoding and distance metric

approach

As depicted in the previous section, the captured muzzle

point images have several significant challenges due to the

unconstrained environment, such as poor illumination,

reduced image quality, low contrast, and blurred. There-

fore, existing feature extraction, representation, and simi-

larity matching algorithms are unable to perform the

recognition of cattle using muzzle point images [7].

To achieve the recognition of cattle, learning-based

feature extraction and matching algorithms are essential to

cater an explicit encoding mechanism of feature space to

improve the recognition accuracy of individual cattle in the

real-time scenario. Therefore, the proposed cattle

Fig. 10 Segmentation of

muzzle point image using

K-means color segmentation

technique

Fig. 11 Illustrates segmentation of muzzle point image


123

recognition approach is motivated by feature representa-

tion, learning-based feature extraction, and distance metric

learning-based matching of muzzle point feature vectors.

In this subsection, learning based on distance metrics

one-shot similarity (OSS) using Fisher linear discriminant

analysis (FLDA), and learning-based distance matrices via

OSS similarity matching technique with first-class online

incremental SVM classification models are applied in the

proposed real-time cattle recognition system.

The 1-class online incremental SVM (1-online ISVM)

model is used to classify the extracted texture features of

muzzle point images. We have done the customization in the

batch formulation of 1-class online incremental SVM (1-on-

line ISVM)model. The formulation customization provides a

method to increase or decrease the sample images of muzzle

point pattern for the better learning the model and classify the

extracted texture features of muzzle point images. The cus-

tomization caters the number of available incremental sam-

ples of muzzle images for recognition of cattle.

The major shortcoming of second-class-based SVM clas-

sification and learning model is that it always requires many

labeled sample of muzzle point images from the two classes.

while first-class SVM classification and learning model

is faster and performs better classification of extracting

features than second-class SVM classification and learning

model. However, it is more suitable for incremental update

of the database in real time [15].

4.3.1 One-shot similarity (OSS) using FLDA

A metric function was defined as a definite positive dis-

tance measurement function for the calculation of distances

between two elements of a given set. For a given set S,

metric function Dð Þ : S� S ! 0; 1½ � is defined. It carries

out distance function that applied to measure the dissimi-

larity between two elements in the given sets S [54].

On the other hand, classical distance-based metric

measure functions, such as Euclidean distance function and

Chi-square distance measure v2ð Þ learning-based metric

expostulate only a set of discrete binary (0 or 1) labeled

pairs of elements or instances from a set S. It can also apply

a distance measure function for learning and classifying

given instances [54, 64].

One-shot similarity (OSS) matching is a semi-super-

vised-based matching similarity technique. It selects the

unlabeled training data as a set of negative constraints

against which two input sample images of muzzle point

pattern are matched [61].

The OSS-based distance function measures the dissim-

ilarity between the given case of muzzle point images and

separates the class of negative instances of muzzle images

that are presented during the matching process of muzzle

point images in the testing phase of proposed cattle

recognition system. The OSS-based matching of distance

between the classes is computed using the classifier shown

in Fig. 12. The output is obtained using each muzzle

instance and negative background set (L).

Generally, during the matching of a given pair of muzzle

image’s instances, let (A) and (B) are the two instances of

the muzzle point images. First, an instance-specific model

of SVM classifier is trained with instance (A) as a single

positive sample image of muzzle point and a separate set of

muzzle point image (L). The sample images are not (A) and

(B) as shown in Fig. 12 of negative samples. A similarity

matching-based index between (A) and set (L) is computed

as S1ð Þ. Similarly, a similarity matching index is trained

and commuted between (B) and the same set (L) as S2ð Þ.The OSS technique computes the similarity between the

set of scores S1ð Þ and S2ð Þ using the summation rule.

Therefore, the formulation based on Fisher linear dis-

criminant analysis (FLDA) is applied as the supervised

classifier technique to classify and recognize the extracted

features. The availability of only one element in the posi-

tive class restricts the number of possible classifiers that

can be used [36].

The FLDA classification technique is used for learning

distance matrix to obtain the advantage of pre-computation

of covariance matrix from all muzzle point image pattern

of within-classes [62]. The computation using FLDA

classification technique is given in brief as follows:

Fig. 12 One-shot similarly

(OSS)-based matching for A and

B two instances of muzzle

images is matched


123

Let Si 2 Rd where i ¼ 1; 2; 3; � � � ;Nð Þ is the set of pos-

itive sample images of muzzle point and Mj 2 Rd, where

j ¼ ð1; 2; 3; � � � ;KÞ is the set of other classes of sample

images of muzzle point with mean values lr ls and l(mean of all points in the muzzle images classes), respec-

tively. According to [64], between-class SBð Þ and within-

class SWð Þ of the muzzle point image database are defined

as follows (shown in Eqs. 1 and 2):

SB ¼ lr � lsð Þ lr � lsð ÞT ð1Þ

SW ¼ 1

N þ Kð ÞXN

i¼1

Pi � lsð Þ Pi � lsð ÞTþQ ð2Þ

where Q ¼ 1ðNþKÞ

Pj¼1ðKðMi � lsÞ � ðMi � lsÞÞT :

The FLDA technique first computes a linear projection

u. It maximizes the scatter matrix separation of between

classes of muzzle images by minimizing the within classes.

The computation is given as follows:

WOPT ¼ argmaxuuTSBu

uTSWuð3Þ

In the linear projection, L keeps a single sample of muzzle

images (e.g., (A) or (B)) and N is a separate set classes of

muzzle image. For the recognition of cattle using muzzle

image (x), a similarity score ismeasured as uT xð Þ � u0, where

u0ð Þ is defined as bias and given as u0 ¼ uTð Þ � xþlrð Þ2

.

Therefore, similarity between scores is measured and com-

puted from these set of sample image (background (L) sam-

ple) using OSS learning and distance metric technique can

improve the matching performance for cattle recognition in

real-time scenario [(L) in the example shown in Fig. 12].

5 Feature extraction and matching

The features are set of pixel intensity values of the given

object in the image. The large set of pixel values is encoded

and transformed into a reduced set of pixel values for

representation of images. The reduced set of pixel values is

known as feature vectors.

As motioned in the previous section, cattle recognition

using muzzle point images has significant challenges that

arise from their continuous movement of the head, pose,

posture, and deformation of animals body during data

acquisition. Therefore, existing feature handcraft extrac-

tion, representation, and matching techniques are unable to

perform the better recognition in feature space [8].

In such scenario of the unconstrained environment,

learning-based feature extraction, representation, and

matching algorithms are essential to perform the encoding

of the extracted feature of muzzle point images and to

provide a better recognition rate of cattle in the real time.

The proposed cattle recognition approach is motivated

from the feature extraction and representation-based learn-

ing and distance metric learning-based matching techniques.

For this research-based experimentation, the database of

muzzle point of cattle contains 5000 muzzle images from

500 cattle. Each cattle has 10 images with 200� 200 pixels.

For the experimentation and encoding purpose, the

database of muzzle images is grouped into two major

classes, such as between-class SBð Þ and within-class SWð Þof muzzle point image database. For the better recognition

rate for identifying individual cattle, there is a need to

provide a better separability of extracting features of

muzzle point images among these classes.

To achieve better separability between classes, we pro-

pose Fisher locality preserving projections (FLPP)

approach for the maximizing the between-class scatter by

minimizing the within-class scatter of the muzzle point

image database. The proposed FLPP approach is illustrated

in detail in the next subsection.

5.1 Proposed FLPP feature extraction

and representation approach

The proposed FLPP approach determines the directions

that maximize the between-class scatter matrix SBð Þ and

maintain the local geometrical data in feature space. These

advantages provided us motivation to propose an improved

FLDA-based feature extraction and representation of

muzzle point images. The extracted features of the muzzle

point image are represented using proposed feature repre-

sentation approach known as Fisher locality preserving

projections (FLPP).

The proposed FLPP approach utilizes two feature

image-based graphs for the representation of the muzzle

point feature. The first feature graph is used to characterize

the within-class SWð Þ scatter matrix of muzzle point images

compactness, and the second graph is dedicated to the

augmentation of the between-class SBð Þ separability.The proposed FLPP approach determines the global

distribution of within-class SWð Þ scatter and between-class

SBð Þ scatter matrix of the muzzle point image database

locally, such that class separability and neighborhood

structure preservation of muzzle image database are

obtained simultaneously.

The primary motivation of the proposed FLPP approach

is to maximize the between-class SBð Þ scatter by mini-

mizing the within-class SWð Þ scatter of the muzzle point

image database. The objective of FLDA is illustrated as

follows (shown in Eqs. 4–9):

SB ¼XN

i¼1

Ni mi � mð Þ � mi � mð Þð ÞT ð4Þ


123

SW ¼XN

i¼1

X

xk

Ni mi � mð Þ � mi � mð Þð ÞT ð5Þ

where SB, SW and l are defined as a between class, within

class, and mean of classes of muzzle image database,

respectively.

l ¼ 1

N

XN

i¼1

Ni ð6Þ

WOPT ¼ argmaxwuTSBu

uTSWuð7Þ

In many cases, within class is singular; therefore, there is in

need to solve the singular equivalent construction can be

applied:


uT SW þ SBð Þu ð8Þ


uTðSRÞuð9Þ

where SR is defined as total scatter matrix and can be

computed as follows:

SR ¼Xc

i¼1

X

xj2Xi

xj � li� �

� xj � li� �T ð10Þ

In the above Eqs. (4–9), the total scatter matrices depend on

the total mean value of extracting features not on the class

means. Therefore, it is computed without having any

information of class labels. The total scatter matrices can be

illustrated in a generalized Eigenvalue problem as follows:

SBV ¼ KSRV ð11Þ

where Eq. (11) generalizes the solution V of the Eigenvalue-

based problem. It keeps the most discriminant projection

direction in the feature space, where c and n are the total

number of sample images and the number of classes of

muzzle point image pattern, nið Þ is the number of samples in

the ith class. Moreover, X½ � ¼ X1;X2; � � � ;Xn½ � is the data

(feature) matrix. The data matrix contains the extracted bead

and ridge features ofmuzzle images, Xið Þ is the set of samples

related to ith class of muzzle images. lið Þ is the mean of

training samples of the muzzle point image database.

To achieve the maximum separability between the

classes of muzzle point images, initially, the two graphs are

constructed and features are represented using FLPP

algorithm. The representations of muzzle point feature

based on graphs are shown in Algorithm 1. At the begin-

ning, the set of n muzzle point images are defined as X ¼X1;X2; � � � ;Xn½ � in R n�dð Þ.In this paper, we assumed that each muzzle point image

belongs to one of the c classes, such as x ¼ x1; x2; � � � ; xn½ �.The proposed FLPP algorithm applies the OSS similarity

matching-based learning, distance matrices-based match-

ing, and incremental first-class incremental SVM model for

the classification and recognition of muzzle point images of

cattle [15]. The proposed FLPP approach is applied for

maximizing the class separability using graph-based rep-

resentation, shown in Algorithm 1.

The proposed FLPP outperforms the conventional Fisher

linear discriminant analysis (FLDA) and locality


123

preserving projections (LPP) methods. For example, in the

case of LPP, FLPP provides a superior compaction repre-

sentation of the within-class SWð Þ muzzle images by min-

imizing the distances between-class SBð Þ data (features)

and its neighbors of the same class. The representation of

10 different data classes in the scatter plot of muzzle

images is shown in Figs. 13 and 14, respectively.

In this paper, for the comparative analysis of experi-

mental results, texture feature-based descriptor techniques

and appearance-based face recognition method are used.

The feature descriptor methods namely speeded up robust

feature (SURF), local binary pattern (LBP), and appear-

ance-based recognition approaches (e.g., principal com-

ponent analysis (PCA), linear discriminant analysis (LDA),

independent component analysis (ICA) and their various

algorithm are applied for the compartive study of experi-

mental results. The brief description about the texture

feature extraction and appearance-based feature extraction

and representation techniques are discussed in brief in the

next subsection.

5.2 Appearance and texture-based feature

extraction algorithm

Appearance-based face recognition algorithms are classical

feature extraction and data representation technique. It is

widely used in the area of pattern recognition and computer

vision.

In this paper, we have applied the appearance-based

feature extraction and representation approaches for the

recognition of individual cattle. The used appearance-based

approaches are, namely, principal component analysis

(PCA), PCA-LiBSVM [6, 58], linear discriminant analysis

(LDA), independent component analysis (ICA) [4, 39]

ICA-LiBSVM, Batch-ILDA [30] and its modified version

algorithms (i.e., Batch-CCIPCA [60]), LDA-LiBSVM,

ILDA-LiBSVM [30] and local projection preserving

(LPP)-based approaches for recognition of cattle

[19, 23, 53].

The feature of muzzle point images consist of dense skin

texture information. The texture features are varying illu-

mination and poor image quality under unconstrained

environment. For the extraction of texture features from

muzzle images, speeded up robust features (SURF) [5],

local binary pattern (LBP) [1, 49], and scale invariant

feature transform (SIFT) [40] algorithms are applied.

Therefore, extracted texture features are encoded into a

binary pattern for the better matching of muzzle images

and enhancement of recognition accuracy.

Fig. 13 Scatter plot of 10 different pixel value of muzzle classes with

250 samples image of muzzle images using the LLP and FLDA

technique

Fig. 14 Representation of scatter plot of 10 different pixel value of

muzzle point image classes with 250 samples image of muzzle images

using proposed FLPP approach


123

5.3 Matching of muzzle point images

In this subsection, matching of muzzle point images using

learning distance-based matrices matching technique, OSS

via SVM, and learning distance matrices via OSS with

first-class incremental SVM classification techniques is

given in detail [61]. The advantage of incremental SVM

classification model is to provide updates regularly to adapt

the variations in pixel intensity in the extracted features of

muzzle images [15].

Further, a customization in the batch-based learning

formulation of first-class SVM model and Incremental

SVM (I-SVM) model for the incremental or decremented

(online) learning procedure is done. It caters a sufficient

availability of an incremental database of muzzle point

image with time. These techniques successively eliminate a

number of histories of samples and restore several new

samples. The matching of two muzzle images (based on

minutiae feature points) is shown in Algorithm 2.

Figure 15 illustrates the detection of minutiae points and

inverted skeleton image with core point (green color),

lower core points (gold color), bifurcations (blue color for

h 2 0� � 180�½ � and purple for h 2 180� � 360�½ � and ridge

endings (orange color for h 2 0� � 180�½ � and red color for

h 2 180� � 360�½ �. The finding of the segmented ROIs and

suppress of extreme points of muzzle point from the ROIs

of muzzle point images are shown in Fig. 16.

6 Experimental result and analysis

In this section, the experimental results are performed on

inlet core 2 duo GHz computer with 40 GB of RAM. First,

database of muzzle point image is cropped and re-sized

into 200� 200 pixels from the frontal face images of each

cattle. After preprocessing, enhancement, and segmenta-

tion of muzzle point images, pixel intensity and texture-

based muzzle point image features are extracted from the

database using texture feature-based descriptor techniques.

6.1 Performance evaluation

For performance evaluation of experimental results, the

first muzzle database was segmented into following parts:

(1) gallery (training) part and (2) testing part. For the

experimental results, six muzzle images of each cattle

(subject) (i.e., 500 cattle �6 muzzle images of each cattle =

3000) muzzle images are applied for the training purpose

of proposed real-time cattle recognition system. For the

testing phase, four muzzle point images are used as query

image for the matching with stored trained muzzle image

template for the recognition of individual cattle.

The evaluation of rank-1 recognition accuracies is

computed and validation of accuracy is completed on five

times crossvalidation in this experiment. For the compu-

tation of recognition accuracy, texture feature-based

descriptors algorithms, such as SURF [5], LBP [1, 49] and

SIFT [40] and appearance-based face recognition algo-

rithms (PCA [58], LDA [19], ICA [4, 39] and its variants)

are applied to the feature extraction and representation of

muzzle images in feature space.

The experimental results are illustrated in the form of

cumulative match characteristics (CMC) curves. The CMCFig. 15 Detection of minutiae points of muzzle images

Fig. 16 Illustration of segmentation process of muzzle point images to find the ROI


123

curve illustrates the identification accuracy of recognition

systems, how well this system discriminates individual by

identifying in the enrolled muzzle point image database

with respect to a given test or probe muzzle image of cattle.

The recognition accuracy of cattle presented in form of

CMC curve, which is shown in Figs. 17, 18 and 19,

respectively and accuracy is illustrated in Tables 2, 3

and 4, respectively. In this experiment, the performance of

the proposed approach is evaluated, and its comparison is

performed with other existing face recognition algorithms.

The set of salient features is extracted using the

appearance-based face recognition algorithms, such as

PCA, LDA, ICA, and its modified variant algorithms,

respectively. Then, 200 PCA component features and 200

Fisher discriminant feature vectors (sets) were obtained

using PCA and LDA feature extraction techniques. The

number of choosing LDA Fisher component feature vectors

varies from 10 to 50. The number of selected PCA com-

ponent varied from 50 to 200 in intervals of 10. Based on

the chosen features of muzzle point image pattern, the

nearest neighbor classifier is applied to classify the muzzle

features. The recognition accuracy of cattle is illustrated in

Fig. 17.

The LDA algorithm performs better than PCA tech-

nique. The top recognition accuracy of LDA algorithm is

79:95 and minimum recognition accuracy 75:57 using 50

feature vectors; however, PCA techniques yield 75:86

using Eigenfaces with 200 feature sets, because PCA-based

Eigenface technique fails to perform the muzzle point

recognition in low illumination and poor image case. It

achieves the minimum number of principal components

(i.e., maximum variance feature) of extracting muzzle

Fig. 17 Recognition accuracy of cattle using PCA, LDA, ICA, LBP,

SURF, and proposed approach

Fig. 18 Recognition accuracy

of cattle using Batch-CCIPCA,

ICA, IND-CCIPCA, ISVM,

LDA, LDA-LiBSVM, PCA, and

PCA-LiBSVM

Fig. 19 Recognition accuracy of cattle using Batch-iLDA, CCIPCA-

LiBSVM, ICA-LiBSVM, iLDA, and iLDA-LiBSVM algorithm


123

point image features. It yields minimum recognition rates

as compared to the LDA and ICA recognition approaches.

As shown in Table 2, independent component analysis

(ICA) feature representation technique exploits only

higher-order dependencies in the pixel intensity values of

muzzle point images. ICA method mitigates the first and

second-order statistical data (pixel values) by sphering the

data on the computation of covariance matrix. In this

experiment, appearance-based feature extraction and rep-

resentation approaches are also tested on muzzle point

images captured in the unconstrained environment (e.g.,

poor illumination and blurriness). Based on observations

ICA technique performs better recognition of cattle in the

unconstrained environment. It yields the higher recognition

accuracy as compared to PCA (eigenfaces) and LDA face

recognition and representation techniques because ICA

method computes the muzzle point features and selects

only independent features from the extraction features by

illuminating the first and second-order statistical data (pixel

intensity values) of muzzle point images.

The ICA technique performs the computation on pixel

intensity to obtain the higher variance of extracting fea-

tures. Figure 17 illustrates the performance of recognition

algorithms such as PCA, LDA, ICA, SURF, LBP, and

proposed FLPP algorithms for the recognition of muzzle

point image pattern of cattle. The recognition accuracy is

amplified by increasing the number of feature sets of

muzzle images.

Table 3 illustrates the recognition rates of Batch-

CCIPCA, ICA, IND-CCIPCA, ISVM, LDA, LDA-

LiBSVM, PCA, and PCA-LiBSVM for the recognition of

cattle. The matching using one-shot similarity (OSS)-based

learning technique and learning distance-based matrices

approach is incorporated with SVM classification model.

The customized batch formulation in the Incremental SVM

(ISVM) is also applied with PCA and LDA for incremen-

tally updating the sample images of muzzle images.

Therefore, the recognition accuracy is achieved by ISVM

technique which is higher than other algorithms because

the previous history of sample images is regularly updating

the incrementally added sample images online.

The recognition rates of muzzle point images using

Batch-iLDA CCIPCA-LiBSVM,ICA-LiBSVM, iLDA, and

iLDA-LiBSVM algorithm are shown in Table 4. Fig. 19

shows the identification accuracy of batch-CCIPCA,

incremental support vector machine (ISVM), LDA-

LiBSVM, PCA, and PCA-LiBSVM for recognition of the

muzzle point pattern of cattle. It shows that the ISVM

algorithm yields 86.98 % recognition accuracy on other

algorithms.

In Batch-CCIPCA technique, the labeled training sam-

ple images of the muzzle are not available beforehand, it

seeks the required training data and achieved it incre-

mentally. However, in incremental steps, it selects a min-

imum number of features for updating the previous data.

Table 2 Recognition accuracy (%) of cattle using PCA, LDA, ICA,

SURF, LBP, and proposed approach

F-sets PCA LDA ICA LBP SURF Prop.

50 74.39 75.57 86.97 78.68 83.40 86.67

100 79.81 80.64 79.92 82.20 62.10 89.78

150 81.89 84.19 78.97 85.92 60.95 93.83

200 75.86 79.95 87.95 92.95 94.57 96.87

Prop. proposed approach, F-sets feature sets

Table 3 Illustrates the performance of modified appearance algorithms such as Batch-CCIPCA, ICA, IND-CCIPCA, ISVM, LDA, LDA-

LiBSVM, PCA, and PCA-LiBSVM

F-sets Batch-CCIPCA ICA IND-CCIPCA ISVM LDA LDA-LiBSVM PCA PCA-LiBSVM

Recognition accuracy (%)

50 66.67 82.75 50.95 82.40 73.33 74.29 60.25 64.78

100 70.49 84.29 54.32 87.68 76.79 79.95 63.75 68.82

150 74.95 86.34 58.95 90.98 80.95 87.59 66.85 71.86

200 75.86 88.95 62.75 94.95 84.57 91.57 68.58 74.98

Table 4 Recognition rates (%)

of muzzle point images of cattle

based on Batch-iLDA,

CCIPCA-LiBSVM, ICA-

LiBSVM, iLDA, and iLDA-

LiBSVM

F-sets Batch-iLDA CCIPCA-LiBSVM ICA-LiBSVM iLDA iLDA-LiBSVM

Recognition accuracy (%)

50 73.49 74.95 75.79 80.75 70.93

100 77.78 78.93 82.95 84.97 74.92

150 82.85 86.95 88.95 90.95 78.25

200 86.98 89.95 92.95 94.95 85.57


123

Therefore, the previous database is updated with minimum

features. After that, it selects only existing features.

Therefore, Batch-CCIPCA technique yields minimum

recognition accuracy.

The recognition accuracy of independent-CCIPCA

increases with increasing set of features. However, the

number of selected eigenvalues of muzzle point image

decreases in each level of feature sets. Therefore, the

recognition accuracy of the IND-CCIPCA technique is low

as compared to other algorithms. The IND-CCIPCA algo-

rithm leaves top 10 muzzle point image pattern which

shows a minimum variance of extracted sets of features

(e.g., pixel intensity of the muzzle image pattern). The

iLDA-LiBSVM recognition technique provides 85:57

recognition accuracy. The iLDA technique yields 94:95

recognition accuracy which is higher than other used

techniques due to the selection of an updates maximum

number of features at each level of feature sets.

6.2 Impact of recognition time for different cattle

images

In recognition of cattle, we demonstrate the significant

impact of cattle images on the required time for recognition

of cattle (e.g., success rate) of the cattle recognition system.

The cattle recognition time consists of time taken for

processing of cattle images and matching time during

training and testing of cattle images for the recognition and

verification of cattle.

In this result, surveillance camera performs first by

detecting the cattle and capturing the image of cattle. After

that, captured images (different size of cattle images) are

wirelessly transferred to the server of the cattle recognition

system. The recognition time of muzzle images for iden-

tifying cattle in the real-time scenario is shown in Table 5.

Figure 20 illustrates the impact of recognition time for

different muzzle image of cattle. The variation in the data

size of cattle images at the required time of recognition is

increased in different sizes of cattle images.

In Fig. 20, as the size of image increases, the cattle

recognition system needs more time to process and

matching of muzzle features for recognizing and verifying

the individual cattle.

In the process of identification, the system performs

several preprocessing and feature extraction and similarity

matching of muzzle point features. Therefore, in the cattle

recognition system, the large size of data also takes more

time for image preprocessing, enhancement, and feature

extraction. It observed the success rate (recognition rate) of

the proposed cattle recognition system using cattle image-3

[13].

7 Conclusion and future directions

In this paper, we proposed a muzzle point-based cattle

recognition system for identifying the individual cattle in

the real-time scenario. The proposed recognition system

captures the images of cattle by the surveillance camera.

The system extracts the video frames from the captured

video of the surveillance camera. The images of cattle are

transferred to the server of the cattle recognition system by

wireless network (internetwork) technology. This research

presents a current state-of-the-art approach for the recog-

nition, verification, and monitoring of cattle using

surveillance camera based on their muzzle point image

pattern in the real time.

A comprehensive description of proposed Fisher linear

preserving projection (FLPP) approach is applied for

extraction and representation of features of muzzle point

images. The extracted muzzle features are matched using

Table 5 Recognition time for different cattle images using muzzle

point recognition approach for cattle recognition

Image Image size (kB) Recog. time

Recognition time (s)

Cattle image-1 10 11.50










Recog. time recognition time (s)

0

20

40

60

80

100

0 20 40 60 80 100

Succ

ess

rate

in%

.

Data size (image size) in kB.

Cattle Image 1Cattle Image 2Cattle Image 3

Existing work on Image 1Existing work on Image 2Existing work on Image 3

Fig. 20 Impact of recognition time for different sizes of cattle images

for recognition of cattle


123

one-shot similarity and incremental first-class SVM learn-

ing model for the recognition of cattle. The proposed

approach yields 96:87 recognition accuracy.

With the appearance (holistic)-based face recognition

and representation algorithms, independent component

analysis (ICA) recognition technique yields 87:95 of

recognition accuracy. On the other hand, texture descrip-

tor-based recognition algorithms, such as local binary

pattern (LBP) and speeded up robust features (SURF),

yield 92.95 and 94:57 recognition accuracy of cattle,

respectively. The evaluation of experimental results on

5000 muzzle point image database of cattle demonstrates

that real-time recognition and monitoring of cattle are

feasible using muzzle point images. It can provide a

common platform for the recognition, tracking, and iden-

tification of locomotion of postures, behavior, and health

monitoring. The proposed approach can provide efficient

solutions to classical animal recognition systems for animal

identification, registration, and supervision in the real

time.In future, we plan to extend the proposed approach for

cattle recognition in the real time. We would like to include

the following points as part of our future work:

1. We would like to design a robust cattle monitoring

system based on our proposed muzzle point recogni-

tion algorithms that can handle large and surveillance

video and provide better recognition accuracy in the

real-time scenario.

2. We would like to increase the performance of the

proposed system; the muzzle point features can be

fused with that of facial images of cattle for particular

biometric recognition system in real-time.

3. Finally we would like to explore the new possibility of

the particular machine learning approaches to extract

the discriminatory features from the muzzle point and

face image database of cattle. We postulate that it can

be helpful even better experimental results in the

proposed multi-model based cattle recognition system

in the real-time scenario.

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Santosh Kumar received the B. Tech. degree in Computer Science

and Engineering, from the Uttar Pradesh Technical University

(UPTU), in 2008, and M. Tech. in Computer Science and Engineering

from Birla Institute of Technology (B.I.T), Mesra, Ranchi (Jharkhand,

India) in 2012. Currently he is pursuing PhD from the Department of

Computer Science and Engineering, Indian Institute of Technology

(B.H.U.), Varanasi-221005. His research interests span a wide range

of spectrum including Computer vision, Pattern Recognition, Digital

Image Processing, Digital Video Processing, Swarm Intelligence,

Metaheuristics, Bio-inspired Computing, Artificial Intelligence. He

was a recipient of best poster awards at Institute Day, Indian Institute

of Technology (B.H.U.), Varanasi-221005. He is a member of the

Computer Society and the Association for Computing Machinery.

Sanjay Kumar Singh has completed his B. Tech. in Computer Engg.,

M. Tech. in Computer Applications, and PhD in Computer Science

and Engineering. Currently he is an Associated Professor at the

Department of Computer Science and Engineering, IIT BHU,

Varanasi. He is a Certified Novell Engineer (CNE) from Novell

Netware, USA, and a Certified Novell Administrator (CNA) from

Novell Netware, USA. He is a member of LIMSTE, IEE, Interna-

tional Association of Engineers and ISCE. His research areas include

Biometrics, Computer Vision, Image Processing, Video Processing,

Pattern Recognition, and Artificial Intelligence. He has over 50

national and international journal publications, book chapters, and

conference papers. He is also a Guest Editorial Board Member of

Multimedia Application and Tools, Springer, and the EURASIP

Journal of Image and Vision Processing (Springer). He is a member of

the Computer Society and the Association for Computing Machinery.

Ravi Shankar Singh is an Assistant Professor in the Department of

Computer Science and Engineering, (B.H.U.), Varanasi, India, since

2004. His research interests include tasks scheduling techniques in

distributed computing, data structures, parallel algorithms. He is

member of ACM and Computer Society of India. He has presented

and published over30 research papers in various national and

international conferences.

Amit Kumar Singh is currently working as Assistant Professor in the

Department of Computer Science and Engineering at Jaypee Univer-

sity of Information Technology (JUIT) Waknaghat, Solan, Himachal

Pradesh, India, since April 2008. He has completed his PhD degree

from the Department of Computer Engineering, NIT Kurukshetra,

Haryana, in 2015. He has presented and published over 40 research

papers in reputed journals and various national and international

conferences. His important research contributions includes to develop

watermarking methods that offer a good trade-off between major

parameters, i.e., perceptual quality, robustness, embedding capacity,

and the security of the watermark embedding into the cover digital

images. Recently, he has appointed as member of editorial board in

one of the prestigious image processing journal Multimedia tools and

Applications (MTAP). Currently, Dr. Singh has served as Guest

editor, TPC member, and reviewer for various conferences and

reputed journals. His research interests include Data Hiding,

Biometrics and Cryptography.

Shrikant Tiwari received his PhD degree from the Department of

Computer Science and Engineering, Indian Institute of Technology

(B.H.U), India, in 2012. He is an Assistant Professor in the

Department of Computer Science and Engineering, Shri Shankar-

acharya Technical Campus, Junwani, Bhilai, District-Durg, Chattis-

grah, India-490020. His research interests include Image Processing,

Machine Learning, and their applications in biometrics. He has

presented and published over 20 research papers and book chapters in

various national and international conferences.


123



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