Eshima Masashi

Kitami Hirokazu

Komatsu Naohisa

DSpace at Waseda University

SICE Annual Conference in Fukui, August 4-6.2003 Fukui ni ity, Japan 'personal Identification Met hod using the Face Information from a Tracking Image Hirokazu Kitami, Masashi Eshima, Naohisa Komatsu Waseda University, Okubo 3-4-1, Shinjyuku-ku, Tokyo, Japan Abstract:  The biometric recognition using the feature of individuals, such as a fingerprint, a hand, and face recognition, is atracting attent,ion. In this paper, the identification method which can be performed without the need of special operation for recognition to a user are proposed. The proposed method will be used at  a person tracking system. We focus on face information, as the information available for tracking a person. The validity of this proposal method was confirmed in indoor environment with simulation results. Keywords: face information. person identification, eigenface, KL deployment, principal- component analysis, biometrics 1. Introduction In recent years, research on the monitoring system of recognizing and tracking using images from cameras in moving objects, such as a person and vehicles, has been advanced frequently. As the backgroud, the throughput of a computer has been improving and image processing including the abject extraction can be done in real time. It has realized by mobile device, such as GPS, a cel- lular phone, as a moving object tracking system in the outdoors. Person's position acquirement is also pos- sible by the downsizing these devices. On the other hand, the monitoring system in indoors environment is realized when people is monitoring through invasion d e  tection systems ahich are composed of sensors, such as infrared rays, and the image of a monitoring camera. One of Characteristics of using camera in access can- trol system is there is no need to carry any devices for identifing/verifing a person. I%om a viewpoint of a security system, research of the high convenient authentication technology using bio- metrics attracted attention. However, users are often forced to authenticate each time before entering the se- cure area, this causes users Feel st,ress. In this paper, w e  aims identifying a person using the image (tracking image) from a camera in person tracking system. By identifying a person using tracking images, users are free from special operation for authentication, or con- tacting device. Moreover, an identification using track- ing images can select candidates ofthe right persons, so combination of other identification facilities will speed up the total process to identify the person. Images captured from a camera in a tracking system, sizes and directions of faces area always changing. Ad- ditionally, there is a case that a part of body is hidden by some objects. However, even if a person's lower half of the body is hidden, a person's face can be captured in many cases. In this paper, we focus on using the face information in a tracking image , and carryed out person identification. 2. The person identification method using face informa- tion The face recognition system using the geometric feature have been proposed so far. The domain of face part is small in tracking image. And when the angle and altitude of a face changes variously, it becomes very difficult to extract parts, such as eye, nose, and mouth. Moreover, problem occurs when the template matching method is applied to the face recognition. It is men- tioned that calculation load cannot be devoted so much to identification process, since exhaction of a face do- main and tracking process of a person are also needed. By applying the eigenface method, we carried out performing person identification of the face image in a tracking image. In this method, reduction of the amount of calcula- tion is expectable ,by carrying out information compres- sion of the pattern of a face image, because matching process is performed using the pattern vector of a low dimension. 2.1 The feature and flow of the proposal method The feature of this proposal method is shown below. When a person in the distance from a camera , face image is small, and when a person is in near, it is large. Moreover, t.he direction of face may change when a per- son walk. Because the direction of person's walking is not fixed. But the system can be necessary to corre- spond also to such a situation. Expansion 01 reduction are carried out for the face im- ages of many sizes, and these images is made the same size. The face images of various angles are prepared for enrolment process, and it corresponds to direction change of a face. object image 1930 PR0001/03/0000-2734WW 0 2003 SICE Feature vector Even when the size of a person's face changes, the pixel value of a person's face itself is used as a feature vector so that the information on a f s x  can be expressed. The pixel value of the face image contains high order feature vector. But high order vectors are compressed to the low dimensions using Principal-component analysis. The flow of the proposal method is shown in Fig.1. The identification me thod  I I I ............. .............. ! I Fig.1: The flow of the proposal method 2.2 Pretreatment Using face images captured by camera, it is assumed that change of the image size, and change of the lumi- nosity value of the face images will occur by the condi- tions of indoor lighting. Luminosity value A face image contains nxn(pixels). When the image is captured, there are also the so-called background im- age which doesn't contain face information. Since this background domain is same (background is covered with white cloth in this experiment environment), the lumi- nosity value of this domain is unified into the average value of all the data for enrolment process, and the lu- minosity value is changed so that the luminosity d u e  of a face image may distribute to 0-255. Size All images are changed into the same size by carrying out expansion-reduction of the face image obtained from the camera. 3. Principal-component analysis As mentioned above, the pixel value of a person's face image is used as a feature vector. Since the size of a image is made into n x n  pixels, one vector has n2- dimensions [ per image of one sheet 1. When a face image is scanned horizontally, the upper left corner of a image is the start coordinates point, and it expresses KL-deployment is used in order to cut down the number of dimensions of the feature x .  The x of I(persons)xJ(fames) is prepared as data for enrolment, and the matrix X which is made by arranging them in a line. x(1) and n2 coordinates points x(i)(j=I,. . .  ,n2). x = [X1,1151,21 . . . X l , J ,  . . . , X i ; j , . . . , x I , J I  (1) F i g 2  The acquisition method of the feature vector The vector m is made as the average vector of the whole X, and the covariance matrix E of matrix X is calculated as shown in (3). N: Number af sheets of a registered image. (I(persons) xd(fames)) E V k  = xxv* (4) About the covariance matrix E. the eigen value A r  and the eigen vector vk  are calculated. The rate r of accu- mulation contribution shown by the following formula is calculated in the large order of A. (5) k is calculated by the rate r of accumulation contri- bution, and the set v of k eigen vector (VI,VZ, .... vk) corresponding to the higher rank accmulation contribu- tion of k pieces are registered into a database as indi- vidual registration data. The vector o is calculated for the feature vectors 1: by the following formula. o = [vl, v 2 , v 3 ,  .... V ~ ] ~ ( X  - m) (6) 1931 0' .IO' 20' 30' 40' 50' 60' 70' 80' 90' 1 2 3 4 5 7 Fig.3 An example of an acquisition image 3.1 Acquisition of face images First, a subject's face is captured and the image data used for enrolment is collected. The angle of a face changes variously in tracking images, so a subject IO- tates in advance. Images are captured under a white background so that it may be easy to carry out extrac- tion process. hloreover, while the person is waking, person's face is not always facing front side. That is, even if he is conscious of facing front, it is possible to  change to the upper or lower sides a bit. hloreover, image data was acquired by having a person rotate by 10-degree un i t  this time. Multi datas of the same angle are acquired, but it might not become 10 degree correctly. The example of the face images acquired per person is shown in Fig. 3. This time, a image is simultaneously taken for enrolment process and identification process. There are five patterns by the mm total per one angle, and pattern 1-2-3 is used for enrolment and pattern 4-5 is used for identification . 3.2 Registration of the data for enrol- ment The eigenface method is applied and eigen space are created by performing principal component analysis. By the repeating method of approximation, a eigen value and a eigen vector are calculable one by one using Power-law-distribution which can improve the solution. The number of dimensions compressed based on the rate of accumulation contribution is decided, and the map o which compressed the original image to eigen space is calculated. When mapped to eigen space, the face im- age of one sheet will correspond to one point. Here, it is mentioned as character of eigen space that the carrela- tion value between original images is equivalent to the distance value on eigen space. It becomes high correla- tion between the faces of adjoining angle and it may be understood also from Fig. 3 The distance of the map o which is a point on the eigen space corresponding to the image becomes small, and the sequence of points draw a smooth series. 4. Evaluation experiment 4.1 Influence by the difference in the number of input data As a stage before mapping a original image to eigen space, principal component analysis by the eigenface method is performed. And there are calculation ofeigen vector and a stage which calculates the rate of accumu- lation contribution. We have investigated what kind of influence in eigen vectors and the rate of accumula- tion contributions occurs by the change of the number of datas. The word, pattern, used here means how to combine, two or more data used for enrolment in the same person and the same angle acquired as shown in Fig. 3. The following identification experiments were carried out by the proposal method. Experiment 1 The images photoed at the equal dis- tance from the camera. Experiment 2 The images whose sizes are differ (zoom function of a DV camera). 4.2 eigen vector In order to examine what change occurs to eigen vec- tors with the increase of the number of patterns, the fol- lowing formula estimated the degree of similar of eigen 1932 Fig.4: eigen vectorilst'5th-principal componentj vectors between using 5 patterns and 1-4 patterns Here, Rj expresses that the degree of similar between the eigen vector ~ ( i , ~ )  using 5 patterns and v(i,j) using the number of patterns used as the candidate for com- parison , whkh equivalent to the j - t h  principal compo- nents. v , , , ~ ~ , ~  and vm,n,5 show the maximum and the minimum value in the element of v , , ~ .  6 shows that The degree of similar of the rate of accumulation contribution of the eigen vector ( ~ 1 ~ ~ 2 ,  ..., u k )  in Consideration created in the experi- ment 1. However, x of data x-y in Fig. 4 is the number of patterns of the data for enrolment shown in Table 1, and there are 100 sheets at  data 1-5, and 200 sheets at data 2-5, . . . , 500 sheets at data 5-5 are learned. Fig. 6 shows the degree of similar as the base of the pat,tern 5 (=v) with most enrolment data. Fig. 4.3 The rate of accumulation contribu- tion We have calculated accumulation contribution from the formula (5) when the number of data of the face im- ages used for principal component analysis is increased from one pattern to five patterns. By calculating the rate of accumulation Contribution, it can measure how much information on the pattern vectors of original im- ages does eigeu vector includes. That is, if the rate of accumulation contribution is high in a law dimension, since the data for enrolment can be registered with the fewer number of dimensions, reduction of the amount of calculation is also expectable. You can understand from Fig. 5 that the more the number of dimensions are in- creased, the more the rate of accumulation contribution changes. Fig. 5 shows that the rate of increase of the rate of contribution becomes low, when the number of patterns (the number of data) of the face images used for Prin- cipal component analysis increases. Moreover, if the number of data is small as used in this experiment, it reaches even to 70% in about 10 dimensions, and even to 80% in about 18 dimensions, mid even to 90% in about 40 dimensions. By this experiment, the pattern vectors of the 4096- dimensional original face images is compressed to the partial sapce of low dimensions which vectors maintain the amount of information , the rate of accumulation contribution indicate. Moreover, although the increase in the rate of contribution is intense in a low dimension, the rate of accumulation contribution is hard to go up as it becomes the component of high order. . This shows that the eigen vector of a low dimension can express more amount of information of a original image. I 0 '  I I 0 1 0 1 0 3 0 4 0 m  Th. n m b w  ot w h d ~ d  mwn.nt  WJ Fig.5: Change of the rate of accumulation contribution 4.4 Evaluation result In experiment 1, it was discriminated that all 10 out of 10 subject persons are identity. Furthermore, in it, the number of sheets of the image judged to be the same angle (the number of correct answers) is shown in Table 2. Moreover, Fig. 6 indicates that almost same eigen vector (eigen space by principal conpanent analysis) as the pattern 5 using many enrolment data has been cre- ated, even when there are few patterns of enrolment data (data 1-5, 2-5), almost same eigen vector (eigen 1933 Table. 1: Experiment element Data for enrolment Angle I Pattern I Number of people I Number of sheets Experiment P I O'SOiEvery 10; I 1'5 I 10 I 100'500 Experiment Q I O'SOiEvery loj  I 1'5 I 5 50'250 Table. 2: The number of correct answers of the angle in experiment l(10 persons) i20 data per individualj person I A I B I C I D I E I F / G I H I I I J  The number of correct answers I 20 I 13 I 18 I 16 1 18 I 18 I 17 I 20 1 16 I 15 space by principal conponent analysis) as the pattern 5. using many enrolment data has been created. How- ever, since the identification result may be influenced if the vertical direction of a face is changed, these two factars are considered a trade-off relation. FIg. 7, based on person A in experiment 2, shows the result of the degree of similarity regarding the rate of accumulation contribution of the vectors between any other person and person A. In this figure, B-A in Fig. 7 shows that the degree of similarity of the data of the person B based on the data of Person A. The results of experiment 2 lead us  to think that the almost same mapping vectors were able to be obtained by inputting the data after normalization of face images of different sizrs. Fig. 7 shows that there is little influence even when the image size of input data changes. hloreover, since there is a clear difference between comparison A- A(between individual) and any other data, it can be said that identification of individual and any others is possible. Fig.8: Reconstruction of a face Fig.6 Experiment. 1 The degree of similar e 70 1 2 3 4 5  P.1t.m W0.l Fig.7 Experiment. 2 The degree of similar [ between individual's Map vector 01 1934 5. Conclusion In the environment where a person's whole body is c a p  tured, we proposed for applying the eigenface method which is a 2-dimensional collation model , we applied this by using the images of w i o u s  angles and sizes in the enrolment process. There are the following char- acteristics in a Tracking image. In an environment where the face domain occupied in the image captured is small, it is hard to extract the features as eyes, nose and mouth, and the direction and size of the face is not consistent. The evaluation experiment proved the adaptability to the tracking image. Moreover, since it is effective also from the regarding the speed of the cal- culation process, it is thought that it is effective also in identification process in real time. In the future, We will focus on the following terms. Increase the number of people for enrolment. The method with which data is compensated fc- cusing on the correlation between angles. Direction presumption processing of the face ob- tained from tracking system Improvement in accuracy by integrating the iden- tification result of multiple frames acquired during tracking Acknowledgment This research was supported (in part) by a Grant-in Aid for the WABOT-HOUSE Project by Gifu Prefec- ture. References 151 Rein-Lien HsuChlohamed Abdel-hlottaleb, Ani K,Jain. "Face Detection in Color Images" IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLI- GENCE, Vol. 24, No. 5, pp. 696-711, 2002. 1935 

A Personal Identification Method using the Face Information from a Tracking Image

Kitami, Hirokazu

48040

Eshima, Masashi

48041

Komatsu, Naohisa

48042

Waseda University Repository

https://waseda.repo.nii.ac.jp/?action=repository_action_common_download&item_id=27608&item_no=1&attribute_id=101&file_no=1

A Personal Identification Method using the Face Information from a Tracking Image

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