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Abstract — This paper introduces the establishment of PolyU Near-Infrared Face Database (PolyU-4

NIRFD) for face recognition. The PolyU-NIRFD contains images from 350 subjects, each 5

contributing about 100 samples with variations of pose, expression, focus, scale and time, etc. In total, 6

35,000 samples were collected in the database. The PolyU-NIRFD provides a platform for 7

researchers to develop and evaluate various near-infrared face recognition techniques under large 8

scale, controlled and uncontrolled conditions. Finally, we provide three protocols to evaluate the 9

baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP (local 10

binary pattern) on the PolyU-NIRFD database. 11

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Index Terms — Near-infrared face recognition, face database, feature extraction 13

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Corresponding author: Lei Zhang (cslzhang@comp.polyu.edu.hk). Tel: 852-27767355. Fax: 852-27740842. Lei Zhang and David Zhang are with the Biometrics Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong. Baochang Zhang is with the School of Automation Science and Electronic Engineering, Beihang University, Beijing, China. Linlin Shen is with the Dept. of Electronic Engineering, Shenzhen University, Shenzhen, China.

Baochang ZHANG, Lei ZHANG, David ZHANG, Linlin SHEN, Zhenhua GUO

PolyU Near-Infrared Face Database

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1. INTRODUCTION 15

Face recognition (FR) is a promising technology for automated personal authentication and it has a great 16

potential in applications of public security, video surveillance, access control and forensics, etc. [R. Chellappa 17

et al. 1995, W. Zhao et al. 2003]. Meanwhile, FR is one of the most active topics in the filed of computer vision, 18

and several large-scale face databases [W. Gao et al. 2008, Enrique et al. 2003, T. Sim et al. 2003, K. Messer, et 19

al. 1999, A. M. Martinez and R. Benavente 1998, A. S. Georghiades et al. 2001, K. C. Lee et al. 2005] are 20

publicly available for evaluating and comparing various FR methods. Generally speaking, FR in visible 21

spectrum has been mostly studied because it is convenient to implement in various environment and has a wide 22

range of applications [R. Chellappa et al. 1995, W. Zhao et al. 2003, Turk and Pentland. 1991, Belhumeur et 23

al. 1997]. Many FR algorithms have been proposed [R. Chellappa et al. 1995, W. Zhao et al. 2003], and the 24

large-scale face databases play an important role in evaluating and developing FR algorithms. 25

Face recognition technology (FERET) [P.J. Phillips et al. 2000], face recognition vendor test (FRVT) [P. J. 26

Phillips et al. 2002], and face recognition grand challenge (FRGC) [P. J. Phillips et al. 2005] have pioneered 27

both evaluation protocols and database construction. FRGC is more changeling than FERET and FRVT, as it 28

contains more uncontrolled variations and 3D images in its database. For example, in the most challenging set 29

of the FRGC v2 (Exp#4), the training set contains 10776 images from 222 subjects, while the query and target 30

sets contain 8,014 and 16,028 images respectively. The 3D training set of FRGC consists of controlled and 31

uncontrolled still images from 943 subject sessions, while the validation partition of FRGC contains images 32

from 466 subjects collected in 4007 subject sessions. Other publicly available face databases include the CAS-33

PEAL [W. Gao et al. 2008], BANCA [Enrique et al. 2003], CMU PIE [T. Sim et al. 2003], XM2VTSDB [K. 34

Messer, et al. 1999], AR [A. M. Martinez and R. Benavente 1998], Yale [Georghiades et al. 2001, Lee et al. 35

2005], etc. These face databases in visible spectrum provide a good evaluation platform for various FR 36

techniques, and in return they greatly facilitate the development of new FR methods. 37

In visual face recognition, the performance suffers from the lighting variations. To solve this problem, 38

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traditional methods are mostly based on Lambertian model [T. Sim et al. 2003], which is too simply to describe 39

the real face surface under various illuminations. Recently, active near-infrared (NIR) FR was proposed to deal 40

with the illumination variations in different environments, and NIR based FR has shown promising performance 41

in real application, which uses different imaging sensors in invisible spectral bands to reduce the affection of 42

ambient light. X. Zou et al. proposed to use active NIR light to localize face areas in the images and then 43

recognize faces [X. Zou et al. 2005]. S.Z. Li et al. extracted the Local Binary Pattern (LBP) feature and used 44

Fisher analysis for NIR based FR, and they developed a complete NIR FR system which can perform face 45

detection, eye localization and face identification [Stan Li et al. 2007]. Pan et al. [Z.H. Pan et al. 2003] proposed 46

an NIR-based FR system which captures face images in wavelength of 0.7um-1.0um. Some other works using 47

NIR images for FR can be found in [J. Dowdall et al. 2003, A. S. Georghiades et al. 2001]. Zou et al. [X. Zou et 48

al. 2005] have shown that FR in NIR band has better performance than that in the visible band, and this was 49

also validated in S.Z. Li’s work [Stan Li et al. 2007]. However, so far there is not a large scale NIR face 50

database which is publicly available. There is a high demand to construct an open NIR FR database, on which 51

the researchers can test and compare their algorithms. In this paper, we will introduce such a database we 52

constructed in the Hong Kong Polytechnic University, and name the database as PolyU Near-infrared Face 53

Database (PolyU-NIRFD). 54

The face images in PolyU-NIRFD were collected from 350 subjects, each subject providing about 100 55

samples. The sample images involve various variations of expression, pose, scale, focus and time, etc. To 56

evaluate the performance of different FR methods on the PolyU-NIRFD, we provide three test protocols, 57

including the partition strategy of the training, gallery and probe sets and the baseline evaluation schemes. The 58

baseline algorithms we used for comparison are Eigenface, Fisherface, Local Binary Pattern (LBP) [T. Ojala et 59

al. 2002] and their Gabor filtering enhanced versions, which are well-known and representative methods in the 60

field of FR. Considering that the Gabor filtering can improve significantly the FR accuracy (e.g. Gabor 61

Fisherface and Gabor LBP are among the best FR algorithms), we also examine its performance on PolyU-62

NIRFD. 63

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The rest of the paper is organized as follows. Section II introduces the developed NIR face acquisition 64

system and the establishment of PolyU-NIRFD. In Section III, the baseline algorithms are briefly described. 65

Section IV presents extensive experiments using three protocols to evaluate the performance of various FR 66

methods, including Gabor-Eigenface, Gabor-Fisherface, Gabor-LBP, on the PolyU-NIRFD. Conclusions are 67

drawn in Section V. 68

69

2. POLYU-NIRFD CONSTRUCTION 70

Different from the FR in visible band, which can simply use a common camera to capture face images, 71

FR in NIR band needs some additional hardware and special system design for image acquisition. This 72

section describes the NIR image acquisition system and the collection of the PolyU-NIRFD database. 73

74

2.1. Near-Infrared Face Image Acquisition 75

The hardware of our NIR face image acquisition system consists of a camera, an LED (light emitting diode) 76

light source, a filter, a frame grabber card and a computer. A snapshot of the constructed imaging system is 77

shown in Fig. 1. The camera used is a JAI camera, which is sensitive to NIR band. The active light source is in 78

the NIR spectrum between 780nm - 1,100 nm and it is mounted on the camera. The peak wavelength is 850nm, 79

and it lies in the invisible and reflective light range of the electromagnetic spectrum. An NIR LED array is used 80

as the active radiation sources, and it is strong enough for indoor use. The LEDs are arranged in a circle and 81

they are mounted on the camera to make the illumination on the face is as homogeneous as possible. The 82

strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera-83

face distance is between 80cm-120cm, which is convenient for the users. When mounted on the camera, the 84

LEDs are approximately coaxial to the imaging direction and thus provide the best possible straight frontal 85

lighting. Although NIR is invisible to the naked eyes, many CCD cameras have sufficient response to the NIR 86

spectrum. The filter we used in the device is used to cut off the visible light, whose spectrum is shorter (780nm, 87

5

visible light). For the convenience of data collection, we put the imaging device into a black box of 19cm width, 88

19cm long, and 20cm high, as shown in Figure 1. 89

90

91 Fig. 1: The NIR face image acquisition device. ‘A’ is the NIR LED light source, and ‘B’ is the NIR sensitive camera 92

with a NIR filter. 93

94

2.2. PolyU-NIRFD Construction 95

By using the self-designed data acquisition device described in Section II-A, we collected NIR face images 96

from 350 subjects. During the recording, the subject was first asked to sit in front of the camera, and the normal 97

frontal face images of him/her were collected. Then the subject was asked to make expression and pose changes 98

and the corresponding images were collected. To collect face images with scale variations, we asked the 99

subjects to move near to or away from the camera in a certain range. At last, to collect face images with time 100

variations, samples from 15 subjects were collected at two different times with an interval of more than two 101

months. In each recording, we collected about 100 images from each subject, and in total 35,000 images were 102

collected in the PolyU-NIRFD database. The sample images in the PolyU-NIRFD are labeled as 103

‘NN_xxxxxx_S_D_****’, where “NN” represents the prefix of the label string, ‘S’ represents the Gender 104

information, ‘xxxxxx’ indicates the ID serial number of the subject, ‘D’ denotes the place where the image was 105

captured, and ‘****’ is the index of the face image. For example, “NN_200001_F_B_024” means that the 24th 106

image is from a Female subject collected in Beihang University. Figure 2 shows some face images of a subject 107

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with variations of expression, pose and scale. Figure 3 shows the images of some subjects which were taken in 108

different times. 109

110

111 a) b) 112

113 c) d) 114

Fig. 2: Sample NIR face images of a subject. (a) Normal face image; and images with (b) expression variation; (c) 115 pose variation and (d) scaling variation. 116

117

118

119 Fig. 3: Sample NIR face images captured in more than two months. 120

121

To evaluate the performance of different methods on the PolyU-NIRFD, we design three types of 122

experiments, each of which contains a training set, a target (Gallery) set and a query (Probe) set. In Exp#1, the 123

used images include frontal face images as well as images with expression variations, scale changes (include 124

7

blurring), and time difference, etc. In this experiment, the sizes of the training set, target set and query set are 125

419, 574 and 2763, respectively. In Exp#2, we add more faces captured in uncontrolled conditions to make the 126

test more challenging. The sizes of the training set, target set and query set are 1876, 1159, and 4747 127

respectively. In Exp#3, we focus on the images with high pose variations and exclude the images with 128

expression, scale and time variations. The sizes of the training set, target set and query set are 578, 951, and 129

3648, respectively. In next section, we will present baseline face recognition algorithm, and then in Section IV 130

the three types of experiments will be conducted by different methods. 131

132

3. BASELINE ALGORITHMS 133

We choose both grey-level image and Gabor based Eigenface, Fisherface, and the LBP method as the 134

baseline algorithms. 135

3.1 Gabor Wavelet 136

In image processing and object recognition, Gabor features are widely used for image feature descriptors 137

extracted by a set of Gabor wavelets (kernels) which model the receptive field profiles of cortical simple cells. 138

They can capture salient visual properties in an image, such as spatial characteristics, because the kernels can 139

selectively enhance features in certain scales and orientations. The Gabor wavelets (kernels, filters) can be 140

defined as follows: 141

2 2 2 2, ,

2( || || || || / 2 ), / 2

, 2

|| ||ψ ( ) u v u viu v

u v e e eσ σ

σ− −⎡ ⎤= −⎣ ⎦

k z k zkz , (1) 142

where ( )xy=z , ( ) ( ) / 2cos

, sin max max, / 2 , , 0,..., -1, 0,..., -18

vjx v u

jy v u

k ku v k k v uk u v v u uφ

φππ φ= = = = = =k , v is the 143

frequency, u is the orientation, max max5, 8 and 2v u σ π= = = . In this paper, only magnitude part of the Gabor 144

feature is used to enhance the performance of Eigenface, Fisherface, and LBP. 145

146

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3.2 Baseline Algorithm 147 148

Principal Components Analysis (PCA) is commonly used for dimensionality reduction in signal 149

processing, also known as Eigenface in face recognition. Eigenface serves as the baseline method due to its easy 150

implementation and reasonable performance. PCA chooses projection directions that maximize the total scatter 151

across all images of all faces in the training set, and the scatter matrix is calculated as: 152

_ _

1( )( )( )

CT

i i ii

p x x x x x=

= − −∑S (2) 153

Linear Discrimiant Analysis (LDA) is a widely used method for feature extraction and dimensionality 154

reduction in face recognition, which is the core part of the well-known Fisherface method. LDA tries to find the 155

discriminative project direction in which training samples belonging to different classes are separated. 156

Mathematically, it calculates the projection matrix in a way that the ratio of the determinant of the between-157

class scatter matrix of the projected samples and the within-class scatter matrix of the projected samples is 158

maximized. The between-class scatter matrix bS and within-class scatter matrix wS are defined as follows: 159

1( )( )( )

CT

b i i ii

p u u u uϖ=

= − −∑S , (3) 160

1( ) {(( )( ) ) | }

CT

w i i i i i ii

p E x u x uϖ ϖ=

= − −∑S , (4) 161

where 1

(1/ )in

i ijj

u n x=

= ∑ denotes the sample mean of class i , u is the mean of all training images, and ( )ip ϖ is the 162

prior probability. 163

In this paper, we also test the LBP based face recognition on the PolyU-NIRFD database. Derived from a 164

general definition of texture in a local neighborhood, LBP is defined as a gray-scale invariant texture measure 165

and is a useful tool to model texture images. LBP later has shown excellent performance in many comparative 166

studies, in terms of both speed and discrimination performance. The original LBP operator labels the pixels of 167

an image by thresholding the 3 3× neighborhood of each pixel with the value of the central pixel and 168

concatenating the results to form a number. The thresholding function ()f for the basic LBP can be formally 169

9

represented as: 170

00

0

0, ( ) ( )( ( ), ( ))

1, ( ) ( )i

ii

if I Z I Z thresholdf I Z I Z

if I Z I Z threshold− ≤⎧

= ⎨ − >⎩ (5) 171

where , 1,...,8iZ i = is an 8-neighborhood point around 0Z as shown in the central part in Fig. 4. A LBP can 172

also be considered as the concatenation of the binary gradient directions, and is called a micro-pattern. Fig. 5 173

shows an example of obtaining a LBP micro-pattern when the threshold is set to zero. The histograms of these 174

micro-patterns contain information of the distribution of the edges, spots, and other local features in an image. 175

1Z 2Z 3Z

8Z 0Z 4Z

7Z 6Z 5Z 176

Fig. 4. An example of 8-neighborhood around 0Z . 177 178

179

Fig. 5. An example of obtaining the LBP micro-pattern for the region in the black square. 180

181

4. EXPERIMENTS 182

The main objectives of the experiments in this section are to evaluate the performance of well-known face 183

recognition algorithms on the PolyU-NIRDF and investigate the strengths and weaknesses of the proposed 184

method and baseline algorithms. We choose the Eigenface (i.e. PCA) [Turk and Pentland. 1991], Fisherface 185

(i.e. LDA) [Belhumeur et al. 1997], LBP [T. Ahonen et al. 2006], Gabor-PCA, Gabor-LDA and Gabor-LBP as 186

the baseline algorithms. The Gabor-LDA and Gabor-LBP are among the best FR methods and they are 187

benchmarks to evaluate the performance of FR techniques. 188

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4.1 Experiment 1 189

In Exp#1, we set up a subset from the whole PolyU-NIRFD database. In this subset, the training set contains 190

419 frontal images from 138 subjects, while the gallery set and probe set have 574 and 2763 images 191

respectively. No images in the probe and gallery sets are contained in the training set. The facial portion of each 192

original image is automatically cropped according to the location of the eyes. The cropped face is then 193

normalized to 64 64× pixels. The eight methods are then applied to this dataset to evaluate their performance. 194

For subspace based methods, the distance used in the nearest neighbor classifier is the cosine of the angle 195

between two feature vectors. For LBP histogram features, we use the histogram intersection similarity measure. 196

The sub-region size and the number of histogram bins for LBP are 8x8 and 32. Since the Gabor filtering is 197

performed on five scales and eight orientations, we need to downsample the response image to reduce data 198

amount. The downsampling factor is set to 4. Therefore, for Gabor-PCA and Gabor-LDA, the input signal size 199

is 264 64 5 8 4× × × ÷ =10240. 200

The FR results by the eight methods are illustrated in Figures 7 and 8. For subspace based methods PCA, 201

LDA, Gabor-PCA and Gabor-LDA, the curves of recognition rate versus feature vector dimensionality are 202

plotted in Figure 7. We see that the curves for PCA and LDA are flat when the dimension of feature vector 203

changes from 60 to 120. In this experiment PCA gets similar performance to LDA because the number of 204

training samples for each class is limited. From Figures 7 and 8 we can clearly see that using Gabor features can 205

improve greatly the performance of all the four methods. For example, the recognition rate of Gabor-LDA is 206

10% higher than that of LDA. Comparing Figure 7 with Figure 8, it can be seen that the LBP method achieve 207

better performance than PCA and LDA. 208

209

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210 Fig. 7: Recognition results by PCA, LDA, Gabor-PCA and Gabor-LDA in Exp#1. 211

212

213 Fig. 8: Recognition results by LBP, Gabor-LBP in Exp#1. 214

215

4.2 Experiment 2 216

In Exp#2, we extracted from the whole database a much bigger subset than in Exp#1. In this subset, the training 217

set contains 1876 frontal images of 150 subjects, while the gallery and probe sets have 1159 and 4747 images, 218

respectively. The recognition results of PCA, LDA, and LBP are illustrated in Figure 9 and Figure 10. Different 219

from that in Exp#1, LDA achieves much better performance than PCA when using a small number of features. 220

Similar to those in Exp#1, Gabor based methods get much better performances than their original image based 221

12

counterparts. We can also observe that the best result of Gabor-LDA is 92.1%, which is not as good as in exp#1. 222

This is because the dataset in Exp#2 is more challenging by involving more variations of pose, expression and 223

scale than that in Exp#1. We can see that LBP has much better performance than PCA and LDA, which 224

validates again that LBP is effective way to model infrared face images. 225

226

227

Fig. 9: Recognition results by PCA, LDA, Gabor-PCA and Gabor-LDA in Exp#2. 228

229

230

Fig. 10: Recognition results by LBP, Gabor-LBP in Exp#2. 231

232

233

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4.3 Experiment 3 234

Exp#3 is designed to evaluate the performance of the algorithms on the large variations of pose, expression, 235

illumination and scale, etc. In this subset, training set contains 578 images from 188 subjects, while gallery and 236

probe sets have 951 and 3648 images individually. The results are shown in Figures 11 and 12. 237

238

239 Fig. 11: Recognition results by PCA, LDA, Gabor-PCA and Gabor-LDA in Exp#3. 240

241

80

82

84

86

88

90

92

94

8 16 32 64 128

LBP

Gabor-LBP

242

Fig. 12: Recognition results by LBP, Gabor-LBP in Exp#3. 243

244

245

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5. CONCLUSIONS 246

We introduced in this paper the establishment of PolyU near infrared face database (PolyU-NIRFD), which is 247

one of the largest NIR face databases so far. The main characteristics of the PolyU-NIRFD lie in two aspects. 248

First is its large scale. It consists of 35,000 images from 350 subjects. Second is its diversity of variations. It 249

includes variations of pose, expression, illumination, scale, blurring and the combination of them. Comparative 250

study of baseline algorithms was performed on the PolyU-NIRFD to verify its effectiveness. In the future we 251

will acquire a larger database under both controlled and uncontrolled environment, and perform more 252

experiments to investigate more effective NIR FR algorithms. 253

254

6. OBTAINING THE POLYU-NIRFD 255

The PolyU-NIRFD will be publically available soon. The information about how to obtain a copy of the 256

database will be found on the website (http://www.comp.polyu.edu.hk/~biometrics/polyudb_face.htm.) 257

258

ACKNOWLEDGMENT 259

The work is supported by the GRF grant of HKSAR, the central fund from Hong Kong Polytechnic 260

University, the Natural Science Foundation of China (NSFC) under Contract No. 60620160097, and 261

the National High-Tech Research and Development Plan of China (863) under Contract No. 2006AA01Z193 262

and 2007AA01Z195. 263

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