A Near-Infrared Face Detection and Recognition

System Using ASM and PCA+LDA

Peiyi Shen 1, Liang Zhang

1, Juan Song

1, Hu Xu

1, Lianjie Qin

1, Wei Wei

2, Wenzheng Zhang

3, Bin Leng

3,

and Mengqi Zeng 3

1. National school of Software, Xidian University, Xi’an 710071, P. R. China

2. School of Computer Science and Engineering, Xian University of Technology, 710048, PR China

3. Science and Tech. On Com. Security Lab, Chengdu, 610041, P. R. China

Abstract—Near-infrared (NIR) images have a lot of

advantages, which can make up the shortages of visible light

(VL) images. A novel NIR detection and recognition system

is presented in this paper. First, a NIR imaging system is

developed to provide good illumination conditions for

subsequent face detection and recognition. Then, Active

Shape Model (ASM) method is applied to detect the features

and the low-dimensional Gabor features are extracted for

recognition by combining Principal Component Analysis

(PCA) and Linear Discriminant Analysis (LDA). The

recognition rate of the system is up to 90% in a few

milliseconds. Finally, by using such real-time NIR face

recognition system, comparative results are provided, from

that we can seen that this system can work robustly and

effectively.

Index Terms—NIR; ASM; Gabor; PCA; LDA

I. INTRODUCTION

Most current face recognition systems are based on

face images captured under visible light (VL) condition,

which cannot provide accurate recognition due to changes

of environmental illumination [1]. As the Near-infrared

(NIR) images have a lot of advantages such as strong

anti-interference and independence to VL source, great attentions have been paid to face recognition for NIR

images.

Accurate face position is important to feature

extraction for face recognition. During past 20 years,

scholars have carried out a lot of research. Active Shape

Model (ASM) proposed by Cootes [2] has been a popular

object detection method. It can restrict the adjustment of

parameters according to the training data, thereby limiting the shape change in a reasonable range.

Today, the methods of feature extraction and

description in face recognition are divided into two

categories [3]: geometrical characteristics based and

statistical characteristics based. Recent years, most of the

proposed methods are based on statistical characteristics,

for example, Principal Components Analysis (PCA),

Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network (ANN)

and etc. Among them, PCA and LDA methods based on

subspace are widely used. However, PCA is less sensitive

to the classification information of different training

samples, and LDA is fully considerate of the

classification information, but the calculation process of

LDA is too complex to ensure the accuracy.

To achieve illumination invariant face recognition, we present a novel solution using NIR imaging techniques in

this paper. The solution consists of NIR imaging

hardware and NIR-based face detection and recognition

algorithms.

The remainder of this paper is organized as follows.

Section 2 introduces the presented imaging hardware

system for producing face images of a good illumination

condition. Section 3 describes the method of face detection and recognition. Experiments and results are

provided and analyzed in Section 4, prior to discussion

and conclusion in Section 5.

II. NIR IMAGING SYSTEM

The NIR imaging system is developed to overcome the

problem arising from uncontrolled environmental lights

so as to produce face images of a good illumination

condition for face detection and recognition. The key problems to capturing NIR images with good

illumination condition are 1) the design of camera

module and 2) the arrangement of the LED lamps.

To solve the first problem, we choose LED lamps

which can emit 940nm NIR lights as active radiation

source, which are strong enough to produce a clear

frontal-lighted face image without causing disturbance to

the human eyes. As shown in Figure 1(b), we place a optical filter on the camera to cut off the visible

environmental lights, while allowing most of the 940nm

NIR light to pass.

Cameras

A: the lamp panel

Cameras

A: the lamp panel

DD

C: IC/ID card readerC: IC/ID card reader

B: the touch screenB: the touch screen

PCB

PCB

Baffle plateBaffle plate

NIR camera module

NIR camera module

Color camera module

Color camera module

Lamp panel

Optical filter

Lens shadespacer

(1) (2)

Figure 1. The structure of the NIR imaging system. In the figure (1),

the front view of the system. In the figure (2), the side view of the

system

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For the purpose of providing homogeneous light on the

face, it is important to carefully design the layout of the

LEDs. After simulation experiments, the solution of the

second problem is to choose the number of LEDs as 24,

to arrange LEDs around the cameras as shown in Figure

2(a), and to fix each LED lamp with an angle ϕ as shown

in Figure 2(b). With the design above, there exists an image area with homogeneous illumination about 60-

80cm distance from the camera.

(1)

(2)

Figure 2. The architecture of the NIR lighting equipment. In the figure

(1), 24 LEDs which can emit 940nm NIT are placed in the way that the

NIR light can be projected onto a person’s face equally. In the figure (2),

each LED lamp has a fixed angle ϕ

After analysis of the problems, we design the hardware

structure of the NIR lighting system including: the NIR

lighting equipment, cameras, image sensor (CMOS

OV7725) and CPU (OMAP3530). The NIR lighting equipment can provide a stable infrared illumination

condition. There are two cameras to collect both the VL

and NIR images, which can be transmitted to the CPU

through an image sensor and processed using algorithms

presented in Section 3. The structure of the system is

shown in Figure 1.

The NIR lighting equipment can provide a maximum

frame number of 30 per second and produce480 640 images.

(1) The color images

(2) The NIR images

Figure 3. The VL images and their corresponding NIR images are

taken by the present NIR imaging system. The influence of the

environmental lighting on the VL images is obvious, oppositely, the

NIR images can minimum the effect of the bad lighting condition

Figure 3 shows example images of one person taken by

the color camera and the NIR camera with different

direction of environmental illumination. It can be seen

that the VL images are intensely influenced by the

lighting condition, which is adverse to the further face

recognition; while the NIR images are robust to the

changes of illumination with the NIR LEDs lighting in the front and the filter to cut off most of the visible lights.

In this way, the NIR images produced by the equipment

can provide a good foundation of face recognition.

III. DESCRIPTION OF ALGORITHMS

A. ASM Method

The primary task of developing a real-time face

detection and recognition system is to find a suitable face

detection algorithm. Active Shape Model (ASM), an

image searching method based on statistical model,

includes four steps [4]: labeling and aligning the training

set, building the global shape model and the local grey

model, and searching the profile of the object iteratively. The advantage of ASM method is that it can restrict the

adjustment of parameters according to the training data,

thereby limiting the shape change in a reasonable range.

The detailed procedures are described in Figure 4.

Training set

Training set

Labeling and aligning the training set

Labeling and aligning the training set

Building the global shape

model

Building the global shape

model

Building the local grey

model

Building the local grey

model

Testing image

The best face profile

Finding the approximate face location

Finding the approximate face location

Initializing the shape model

Initializing the shape model

Searching the best fit for each

feature point

Searching the best fit for each

feature point

Updating the model

parameters

Updating the model

parameters

Converge?Converge?

Y

N

Figure 4. The process of ASM method

The shape model of 2-Dimensional contains 68 feature

points to represent the Point Distribution Model (PDM)

of human face. After labeling the training samples

manually, we align all the samples through similarity

transformation, and then get the average shape. One disadvantage of ASM is that it is more dependent

on the initial point set, so the initial position of the model

is very important. It is usual to get an approximate face

location through some other image processing methods.

A Haar-feature cascade classifier [5] provided by

OpenCV is used to find a rough face location in this

paper. As shown in Figure 5, the initial shape is obtained

after similarity transformation from the rough face location. In the meantime, the person should always stand

just before the image acquisition equipment.

Figure 5. The model's initialization. Left: the red box shows the rough

face location. Right: the initial shape

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Another disadvantage of ASM is that it is easy to fall

into local extremum during the searching process. To

avoid this, we do some improvements for searching

method. For each feature point, we select the candidate

points along its normal direction. Through calculating the

Mahalanobis Distance between the candidate points and

feature points, we can find the best fit to the feature points. The Mahalanobis Distance can be described as [6]:

1( ) ( ( ) ) ( ( ) )T

j j j jf i y i y s y i y (1)

where, jy and js are the local grey model of the feature

point j, and y(i) is the normalized grey differential vector

of the candidate point i calculated by the same way to

the grey model. This distance reflects the similarity of the

sample point to the feature point.

Figure 6 shows an iterative process for ASM to search

the best fit to the feature points. After face detecting

using ASM method, we can get an accurate face location as the red box shown in Figure 6(f), for the further use of

face recognition.

(1) (2) (3)

(4) (5) (6)

Figure 6. An Iterative Process of improved ASM: 1) initial location, 2)

after 2 iterations, 3) after 4 iterations, 4) after 8 iterations, 5) after 16

iterations, 6) the detecting result of ASM method

B. PCA+LDA Method Based on Gabor Feature

Face recognition is a process to compare the target face

image with the given face in database, whose purpose is

to reach an identification conclusion. The core of this

process is extracting suitable feature and choosing proper

matching strategy.

On the foundation of the research of VL image based

face recognition and with the consideration of the calculation capability of the presented embedded system,

we put forward a new recognition method suitable to NIR

image based face recognition. The first step is to extract

the Gabor feature of the face image, and the following is

to identify the human face with the combinative method

of PCA and LDA.

The process of face recognition is shown in Figure 7.

The NIR images in face database which is under constructing are all collected through the NIR lighting

equipment developed by our lab. At the current stage, the

database includes 80 persons, and each one has at least 80

representative pictures, for example, different expressions,

face rotating and wearing eyeglasses etc.

Training

Recognizing

The result

Extracting Feature and reducing dimensionality

Pattern recognition

Gabor transform

Gabor transform

Gabor transform and PCA

Gabor transform and PCA

Project on subspace

Project on subspace

PCA and LDA

PCA and LDA

Classify by the nearest-neighbour

rule

Classify by the nearest-neighbour

rule

FaceDatabase

Testing image

Figure 7. The flow chart of face recognition

C. Gabor Feature Extraction

Gabor feature extraction is a method based on Gabor

Filter, which is widely applied in computer vision and

image processing fields because of the high resolution in

time and frequency domain.

The kernel function of Gabor Filter is defined as

follows [7]:

2 2 2 2

, ,

2

, ( /2 ) ( /2)

, 2[ ]

k z izkk

e e e

(2)

where and define the orientation and scale of the

Gabor kernels, ( , )z x y , denotes the norm operator,

and the definition of the wave vector ,k is

,

cos

sin

kk

k

. Where k = maxk / f and / 8 .

maxk is the maximum frequency, and f is the interval

factor between kernel functions in frequency domain.

We choose 5 different scales {0,1,2,3,4} and 8

different directions μ {0,1,2,3,4,5,6,7}, and let σ=2π,

maxk = /2, f = 2 . Substitute the value of these

parameters into the equation, and then we can get a set of

kernel functions , ( )z corresponding to different

direction and scale factors.

Given a face image ( , )I x y , the Gabor features are

extracted by convolving ( , )I x y with , ( )z as follows

[8]:

, ,( ) ( ) ( )O z I z z (3)

, ( )O z is the high-dimensional Gabor features of face

image ( , )I x y after Gabor filtering.

D. Face Recognition Using PCA+LDA

Because the calculation cost of the high-dimensional

data is adverse to the performance of the real-time system,

it is necessary to reduce the dimensionality of the Gabor features obtained above, simultaneously retaining most of

the original feature information.

Principal Components Analysis (PCA) and Linear

Discriminant Analysis (LDA) are two common

dimensionality reduction methods. By the analysis of the

advantages and disadvantages of PCA and LDA [9], we

can conclude that PCA method is less sensitive to the

classification information of different training samples, while LDA is fully considerate of the classification

information, but its calculation process is too complex to

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ensure the accuracy. Therefore, a mixed method

combining PCA and LDA is adopted in this paper.

The steps of PCA+LDA [10] method are as follows:

1) Reduce the dimensionality of original data by PCA

to achieve a low-dimensional feature space of PCA;

2) Calculate the within-class scatter matrix and the

between-class scatter matrix of LDA in PCA subspace; 3) Calculate the best classification subspace of LDA,

and fuse the subspace of LDA and PCA;

4) Project training samples and testing images

separately onto the fusion subspace, get the recognition

features, and accomplish face recognition using the

Nearest Neighbor (NN) Rule.

The NN Rule is based on Cosine Similarity, which is

defined as [11]:

cos ( , )

Tx yx y

x y

(4)

where, vector x and y represent the two classes

remaining to be compared. The smaller cos ( , )x y is, the

more similar the two classes are.

(1) The original images

(2) The matching faces in database

(3) The matching scores

Figure 8. The recognition result: 1) the green frames show the detected

faces by ASM, 2) the best matching faces of the upper images, 3) the

matching scores between upper and lower images in the same column

Figure 8 shows the recognition results using the

PCA+LDA method. It can be seen from the matching

scores that the NN Rule based on Cosine Similarity can

separate among the different classes well. The

recognition rate of the algorithm can reach up to 90%

with different indoor light conditions and training faces

of 33 40 size.

IV. EXPERIMENTS AND RESULTS

Based on the presented hardware and algorithms, a

real-time NIR based face recognition system is built. In

the following, we evaluate the performance of the system

with three experiments.

1) Face detection--ASM method

As shown in Figure 9, the presented ASM could adapt

to various situations, such as rotating, shaping, different

expressions and wearing glasses. The average detecting time in a 480 640 image for ASM is 65ms. Then it can

be concluded that ASM is a suitable face detection

algorithm for NIR images.

Figure 9. Face detection results of ASM: the red blocks show face

rectangle and the green gridding shows feature points on the profile of

the whole face

2) Face recognition--algorithm evaluation on self-built

database

In experiment 2, we set up the subset of 50 persons from the whole face database we have build, with the

training number of each person increasing from 1 to 7

images and the training face images normalized into

33 40 size. The testing set has 2,500 images in total. No

images in the testing set are included in the training set.

Based on these, a comparison among PCA, LDA and the

presented PCA+LDA is done.

0.78

0.8

0.82

0.84

0.86

0.88

0.9

0.92

50 100 150 200 250 300 350

PCA

PCA+LDA

LDA

The

re

cogn

itio

n r

ate

The numbler of training samples

Figure 10. Performance with different number of training samples.

As shown in Figure 10, no matter how many the

training samples are, the recognition rate of PCA+LDA is

higher than that of PCA or LDA. Along with the increase

of the training samples, the recognition rate of all the algorithms is on the rise. When the training number of

each person increases to 6, the recognition rate of

presented PCA+LDA reaches the highest recognition rate

in this experiment.

3) Face recognition--algorithm evaluation on PolyU

Near-Infrared face database (PolyU-NIRFD)

In order to evaluate the performance of the face

recognition algorithms on a much bigger subset than experiment 2, we do experiment 3 using the PolyU Near-

Infrared face database (PolyU-NIRFD) [12], which was

constructed by the Hong Kong Polytechnic University.

The database contains images from 350 subjects, each

contributing about 100 samples with variations of pose,

expression, focus, scale and time, etc. Figure 11 shows

sample images of one person in the database [13-17].

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Figure 11. Parts of NIR face images of a subject in PolyU-NIRFD

In this experiment, we extract 200 subjects from

PolyU-NIRFD, with 6 training samples of each subject.

The testing set contains 80 images of each person, in a

total of 16,000.

As shown in Figure 12, the recognition rate of the

PCA+LDA method in experiment 3 is 89%, which proves that the presented algorithm works well on other NIR

face database.

The recognition rate

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PCA LDA PCA+LDA

Figure 12. Performance of the algorithms on PolyU-NIRFD

4) The performance of the real-time system--

comparison among different illumination conditions

(1) Case A: training images captured by color camera

(2) training images captured by color camera and eliminating the

influence of VL

(3) training images captured by NIR camera

Figure 13. Training samples under different conditions: images in

training set are all captured by the presented hardware device, and

furthermore, we conduct homomorphic filtering and histogram

equalization on images in Case B

Figure 13 shows parts of training images in three cases

to compare the performance of the system under

conditions of VL, eliminating influence of VL and NIR

light. It can be seen that unfavorable lighting is obvious

under VL condition, while the changes of environmental

illumination is almost eliminated under NIR light

condition [18-25].

The training set has 30 persons, in which, each person

has 6 images of 33 40 size for training. We set up the real-time system in the lab and do experiments under

three different cases respectively. During the experiment,

people in the training set stand before the image

acquisition equipment in order. The experimental result is

shown in Table 1. We can see that the presented

algorithms under NIR condition can well satisfy the

requests of real-time system.

TABLE I. ANALYSIS ON EFFECTS OF DIFFERENT ILLUMINATION

CONDITIONS

Situations Detection time recognition time recognition rate

Case A

Case B

Case C

60ms

73ms

65ms

78ms

90ms

75ms

78%

87%

90%

V. DISCUSSION AND CONCLUSION

In this paper, a real-time NIR face recognition system

using ASM for detection and PCA+LDA for recognition

has been proposed. The novel NIR lighting system is

developed to solve the problem of uncontrolled environmental lighting. An accurate face location is

obtained by the ASM method. By combining PCA and

LDA, suitable Gabor features are extracted, thus the

calculation cost is reduced and the face recognition rate is

increased. Experimental results show the robustness and

effectiveness of the presented system.

The proposed system still exist some shortages.

Specular reflection on eyeglasses under NIR condition is a limitation for face recognition. The algorithms

presented are practical but not optimal. Our further work

is to solve these problems.

ACKNOWLEDGMENT

This work is partially supported by the Open Projects

Program of National Laboratory of Pattern Recognition.

The Project is partially supported by Natural Science

Basic Research Plan in Shaanxi Province of China (Program No. 2010JM8005). This program is also

supported by Scientific Research Program Funded by

Shaanxi Provincial Education Department (Program No.

2013JK1139) and Supported by China Postdoctoral

Science Foundation (No. 2013M542370) and supported

by the Specialized Research Fund for the Doctoral

Program of Higher Education of China (Grant No.

20136118120010).And this project is also supported by NSFC Grant (No. 61305109, No.61072105 No.

11226173, No.11301414, No. 61272283, No. 11301414,

No. 61172018), by 863 Program (2013AA014601), by

shannxi Scientific research plan (2013K06-09).

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