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SHIV SHAKTI
International Journal in Multidisciplinary and
Academic Research (SSIJMAR)
Vol. 6, No. 2, April 2017 (ISSN 2278 – 5973)
Estimation of Age Group using Histogram of Oriented
gradients and Neural Network
Arpit Kumar Sharma
Mobile No. 9414342366
Impact Factor = 3.133 (Scientific Journal Impact Factor Value for 2012 by Inno Space
Scientific Journal Impact Factor)
Global Impact Factor (2013)= 0.326 (By GIF)
Indexing:
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ABSTRACT
Requirement of Project: Face images are being increasingly used as additional means of
authentication in applications of high security zone. But with age progression the facial
features changes and the database needs to be updated regularly which is a tedious task.
So we need to address the issue of facial aging and come up with a mechanism that identifies
a person in spite of aging. In my project, effective age group estimation using face features
like texture and shape from human face image are proposed[2]. For better performance, the
geometric features of facial image like wrinkle geography, face angle, left to right eye
distance, eye to nose distance, eye to chin distance and eye to lip distance are calculated.
Based on the texture and shape information, age classification is done using KNN & SVM
algorithm (Best algorithm according to many research paper during my research)."
Proposed System: "In this report, few classification and feature extraction techniques used
for age group classification. In this report first we attempt to combining two type of face
features using haar features extraction (Wrinkle features and Geometrical Features) also used
viola Jones for face detection. Age estimation based on the graphical model structure is
proposed. Three popular features, PCA (Principal Component analysis), HOG and
Haarfeatures, are exploited in our work, and three different graphical model structures
considering spatial information and hidden topics are proposed and implemented. The
experimental results showed that our model performs classification techniques like SVM
(support vector machine), KNN and Neural network and the comparisons between features
extraction algorithm and classification techniques in order to obtain best output. features are
also presented and discussed. Until now, the model we proposed hasn’t been well-tuned, and
we’ll try to improve it for the future works." Research in those areas has been conducted for
more than 30 years. "Traditionally, face recognition uses for identification of documents such
as land registration, passports, driver’s licenses, and recognition of a human in a security
area. [22]
1.1 GENERAL INTRODUCTION
Most of the facial variants such as identity, expression, emotions and gender have been
widely studied, in the case of research of recognition. Automatic age estimation is one such
area that has been rarely explored by the researchers. With the evolution of a human, the
features of the face keep on changing with age. This project is providing a new combine
approach of feature selection for age group classification algorithms. Further this process is
further classified into three main stages: first one is Pre-processing, second is Feature
Extraction (Haar feature extraction), and the last stage is classification. For feature extraction
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phase we used two techniques 1) Wrinkle features and 2) Geometrical features for the face
pattern recognition [11]. We know that Wrinkle features are well enough to differentiate
between the adult and senior, Geometrical features is good to create difference between child
and adult/senior. That is why we used a combine technique of wrinkle and geometrical so that
they can solve each other problems and provide the best output. These two approaches are
defined below:
1.1.1) Geometrical features
This include face angle, left eye to right eye distance, eyeball, eye to nose distance, eye to
chin distance and eye to lip distance that is further calculated by making use of the best
feature selection algorithm
1.1.2) Wrinkle features
Age classification based on the texture and shape information is done by using suggested
hybrid algorithm which includes Fuzzy logic and Neural Network. Depending on a number of
groups age ranges are then classified dynamically using hybridization algorithms
individually.
In our research, most facial features like identity, expression, emotions and gender has
been majorly focused. Automatic age estimation is one area that has been rarely
explored till date. As age increases, the feature of the face keeps on changing. This
project provides a comparison study of classification techniques (SVM, KNN
algorithm) and these falls under the category of the best classification algorithms.
Entire process is divided into three stages: Pre-processing, Feature Extraction (Haar
feature extraction) [75], classification (above mentioned algorithm). Machine learning
phase uses different classification algorithm approach in order to provide the best
solution for pattern recognition. That is why we are using one hybrid technique of
wrinkle and geometrical by which they can solve each other problems and provide the
best results. Here we make use of two important features of the face which are
responsible for age identification. Personal identification and verification has evolved
as an active area of research these days. As biometric characteristics of the individual
are unique person to person, biometric authentication techniques have a great
advantage over traditional authentication techniques. Recognition of face is one of the
widely used biometric methods which are used to identify individuals by their face
features. Face, voice, fingerprint, iris, ear, retina are the most commonly used for
authentication purpose. Research in those areas has been conducted for more than 30
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years. Face recognition is beneficial for identification of documents such as for land
registration, passports, driver’s licenses, and recognition of a human in a security area
[84]. Face images are highly used as additional means of authentication in
applications having high security zone. But with increase in age the facial features
also keep on changes and the database needs to be updated regularly which is one
very tedious task. Hence we need to address this issue of facial aging and come up
with a solution that identifies a person without any age limits. In this thesis, effective
age group estimation using face features like texture and shape from human face
image is proposed. For getting efficient results, the geometric features of the facial
image like wrinkle geography, face angle, left to right eye distance, eye to nose
distance, eye to chin distance and eye to lip distance are calculated [90]. Based on the
texture and shape information, age classification is done by making use of
classification algorithms
2. EXISTING SYSTEM
As we already discussed in literature survey age group classification is one of the
research topics from last few years. Many research already done research on the age
group classification with different algorithms (Surf algorithm, PCA and LDA etc.)
and different classification techniques were used. In the age group classification the
most difficult part is to identify the different pattern of the faces[78]. Many authors
have tried and failed. As per our research many researchers failed to observe the exact
pattern of different age group. Pattern recognition, having two critical task one
features extraction and another is classification. Researcher tried to work hard on the
machine learning algorithm and many of them is ignore features extraction
improvisation. As the result they cannot obtain good output, but all the literature
author work excellent on classification algorithm which include (Neural network,
KNN, SVM and Fuzzy logic)[65].
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Block diagram of proposed work
3.3 FLOW CHART OF AGE CLASSIFICATION
The brief description of each block is described below:
IMAGE DATA
PREPROCESS USING
GEOMETRICAL AND WRINKLE
FEATURE
CLASSIFICATION USING
KNN
CLASSIFICATION PERFOM
ANCE
Image aquisition
• Dataset from THE OPEN UNIVERSITY, ISRAEL
Data preprocess
• Face detect viola jone apply
Find Parts• Find face parts
Data prepration
• Find the face features of the images(Haar feature, PCA and HOG)
Classification
• Apply classification(KNN, SVM and Neural Network.
Performance
• Analysis machine learning perfomance .
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FLOW CHART OF PROPOSED SYSTEM
`
Start
Preprocess and feature extraction
Insert 3class AGE
classification
dataset
Detect Face
Resize image
Performance
Apply
Classification
Exit
Face
Detectio
n
Data Divide
Yes
No NOT A
VALID
DATA
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2.1 FEATURE EXTRACTION
One of the main key issue of any characterization frameworks is to locate an arrangement
of reliable features as the basis for classification. In general these features cam be
categorized into two categories. These are wrinkle features and geometric features. Let us
discuss each one of them in detail[59].
2.2 WRINKLE FEATURES
One of the most important property of wrinkle features is that it determines the age of a
person. Estimation of feature F5 can be done as follows :
F5= (sum of pixels in forehead region / number of pixels in forehead region) + (sum of
pixels in left eyelid region / number of pixels in left eyelid region) + (sum of pixels in
right eyelid region / number of pixels in right eyelid region) + (sum of pixels in left eye
corner region / number of pixels in left eye corner region) + (sum of pixels in right eye
corner region / number of pixels in right eye corner region).
F5 can be estimated by making use of the grid features of face image that is completely
dependent on the wrinkle geography in face image.
For the estimation of F5 features, a few steps have to be followed as discussed below:
As the age keeps on increasing, wrinkles on face turn out to be clearer. Aged individuals
regularly have clear wrinkles on the face in the following areas as mentioned below :
a) The forehead has horizontal furrows.
b) The eye corners have crow’s feet.
c) The cheeks have clear cheekbones, sickle molded pouches, and profound lines
between the cheeks and the upper lips.
Since there are evident changes in wrinkle intensities and even some form clear lines,
thus in this Project we make use of Sobel edge magnitudes, approximating gradient
magnitudes in order to judge the level of wrinkles[15]. The Sobel edge magnitude is
larger, if the pixel belongs to wrinkles. The reason behind the larger magnitude is that the
difference of gray levels is self-evident. From this perspective, a pixel is named as a
wrinkle pixel if its sobel edge size is bigger than some limit. Figure 7 (a) and (c)
demonstrate a youthful grown up and an old grown up[69].
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2.3 GEOMETRICAL FEATURES
As indicated by the investigations of facial representation and emotional cosmetics, there
occurs a lot of change in the facial features as the age keeps on increasing. In this phase,
global features in combination with the grid features are extracted from the face images.
The global features include the distance between two eye balls, chin to eye, nose tip to
eye and eye to lip[76].
By making use of four distance values, there occurs calculation of four features namely
F1, F2, F3 and F4 as mentioned below:
F1 = (distance from left to right eye ball) / (distance from eye to nose).
F2 = (distance from left to right eye ball) / (distance from eye to lip).
F3 = (distance from eye to nose) / (distance from eye to chin).
F4 = (distance from eye to nose) / (distance from eye to lip).
It is clear that new born babies have a number of wrinkles on their faces. The head bone
structure in new born ones is not fully grown. Moreover the ration of primary features is
highly different from those in other life spans. Hence we can conclude that it is more
reliable to use geometric features as compared to wrinkle features when it is to be judged
that whether an image is a baby or not[82].
In case of infants, the head is near a circle. The distance between two eyes is almost equal
to the distance from eyes to mouth. As the head bone grows, the head becomes oval
shaped and accordingly there occurs a sudden increase in the distance from the eyes to the
mouth. Above and beyond the ratio between baby’s eyes and noses is equal to the
distance between noses and mouths which in turn are almost equal to one while as in case
of adults it is larger than 1[88].
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3. CLASSIFICATION (KNN ALGORITHMS)
3.3.1 KNN Classification: The k-nearest neighbor algorithm is a classification algorithm
which classifies an object on the basis of where the majority of the neighbor belongs to
[76]. To choose the number of neighbors is optional and it depends on the users. If k is
equal to 1 then it is classified [10] as a class of neighbor is nearest. Normally the object is
classified on the basis of labels of its k nearest neighbors by finding out the majority vote.
If k is 1, the object is classified as the class of the object which is nearest to it. When there
are two classes, it is considered that k must be an odd integer. However, there can still be
times when k is an odd integer while performing multiclass classification. After
converting each image to a vector image of fixed-length having real numbers, we will
then use the most common distance function for KNN that is Euclidean distance [85].
Fig 3.8 :KNN classification. At the query point of the circle depending on the k value of 1,
5, or 10, the query point can be a rectangle at (a), a diamond at (b), and a triangle at (c).
The KNN is classifies an object where the majority of the neighbor belongs to. The
choice of the number of neighbors is discretionary and up to the choice of the users. If k
is 1 then it is classified [10] whichever class of neighbor is nearest[13].
result = knnclassify(Sample, Training, Group, k)
3.2 Histogram of Oriented Gradients(HoG):
The next step is to extract the features of the hand gesture. This system uses the HoG
descriptor (Histogram oriented gradient) to present the hand shape. HoG descriptor counts
the number of times a gradient orientation occurs in a localized are of the image[22]. It
uses a histogram of intensity gradient to depict the shape of the object. This technique is
resilient under change of shadow and illumination. Due to this, it's a popular method for
hand gesture detection[43].
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The implementation method of the HoG algorithm descriptor is given as follows. Firstly,
the cells are divided into smallest possible regions of an image. These regions are called
cells. For each of these cells, a histogram of of gradient orientations or edge orientations
is computed. Each cell is separated and discreted into corresponding angular bins in
accordance with its gradient orientation. The weighted gradient of each cell is contributed
to its respective angular bin. The adjacent cell with same gradient orientation are grouped
together and these spatial regions are known as blocks[57]. These groupings into blocks
is the basis for histograms' normalization. The normalized group represents the block
histogram which in turn represent the descriptor [21].
3.3) Principal Component Analysis(PCA)
PCA is one of the best available statistical methods available that is used for image
compression and gesture recognition. The basic ideology behind the PCA algorithm is the
reduction of the dimensionality of an image and also maintaining maximum variance. The
features which remain then are the ones relevant for recognition[59].
Whenever there is 2-dimensional data, then due to the presence of more than 2 variables,
the visualization of of the relationships becomes complex. PCA reduces the
dimensionality of the data such that the two actual variables are reduced to less number of
new one dimensional variables which are called Principal Components. This is done by
using a single variable for a group of variables. The principal components are a linear
representation of the actual variables[64].
These principal components can also be represented in the form of vectors called Eigen
Vectors. The Eigen Vectors collectively create a feature space known as Eigen space
which is calculated by the eigen vectors of a co-variance matrix derived from a hand
gesture set. Each input gesture image corresponds to eigen vectors which represents the
feature vector of the image[79].
3.4 SVM (Support Vector Machine)
SVM Classification: A support vector machine (SVM) is a non probabilistic linear binary
classifier, which can analyze input data and predict which of the two classes it belong to. It
works by building a hyper plane separating the two classes which is of higher dimension. A
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good separation is obtained by a hyper plane that is very far from any data point of each class
[11], since further the separation of the data, better the performance[64].
Fig 3.6.4: Shows the formation of hyperplane and also the how the image is classified
between red and blue using SVM.
Fig. 3.6.4(B) Overall flow diagram of project
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CONCLUSION
This thesis thoroughly explains a novel method for the age group classification. Proposed
technique based on wrinkle and geometrical features provides a robust method that identifies
the age group of individuals from a set of different images capturing various aged faces.
From these images features are then extracted such as distances between various face
elements, analysis of wrinkle geography and then calculation are performed for finding out
face angles. The results are then compared at the end to find the best way to calculate age
ranges for the face images present in the database. Based on the observed results, images are
further classified into 3 groups on the basis of SVM and KNN algorithm. It is normally
observed that wrinkle geography feature i.e., F5 provides better results to predict human age
range in comparison to other features. Hence we can conclude that wrinkle geography
analysis is one good approach to estimate human age range for an individual. For better eye
and eyeball detection, images should be captured without spectacles. Viola Jone algorithm
focuses on the front face that is why the image needs to be a straight frontal face. As we are
working on the individual face age group identification so for that purpose image should
contain single human face only. This thesis has shown results with 76% accuracy for two age
group, 64% accuracy for three age group. As the numbers of group are increased for
classification the accuracy of classification is decreased. There is a strong possibility for
further extension of the work which includes extracting more feature points that can improve
accuracy of age group classification. By introducing more features the age range can also be
further enhanced.
FUTURE SCOPE
The future work is to add more category in the field of age group recognition classes to the
given system. Also since the proposed system is limited to classify only for front images, so
modeling 3-D face using various cameras to increase the efficiency of the proposed facial age
recognition system can be used for future work. We can also implement face age detection by
using fuzzy logic and genetic algorithm.
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