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Robust Face Recognition with Light CompensationYea-ShuanHuangl, Yao-Hong ~ s a i ' ,un-Wei shieh2'computer & Communications Research Laboratories

Industrial Technology and Research Institute, [email protected]

2Depxtment of Electrical Engineering, YuanZe University

Abstract. Tbis paper proposes a face recopition method which is based on aGeneralized Probabilistic Descent (GPD) lear-g rule with a three-layer feed-forward network. Tbis method aims to r e c o m e faces in a loosely controlledsweillance environment, which allows (1) large face image rotation (on andout of image plane), (2) different backgrounds, and (3 ) different i llmimt ion.Besides, a novel light compensation approach is desiped to compensate thegray-level differences resulted from different lighting conditions. Experimentsfor three kinds of classifiers (LVQ2, BP, and GPD) have been performed on aITRI face database. GPD with the proposed light compensation approachdisplays the best recopition accuracy among all possible combmation.

1 IntroductionDue to the rapid advance of computer hardware and the continuous progress ofcomputer software, we are looking forward to developing more powerful and kiendlycomputer use models so that computer can serve people in a more active andintelligent way. To this end, the computer naturally needs to have a surveillanceability, which enables it to detect, track, and recognize its surrounding people. Thisresults in the situation that researches on face processing (including detection [I-21,tracking [3], and recognition [4-71) are very prosperous in the last two decad es. Thispaper mainly discusses our research effort on the face recognition (FR) issue.The objective of our research is to develop a FR classifier which can recognizefaces in a loosely controlled surveillance environment. This means the desired FRclassifier can deal with the faces having different rotations, illumination, andbackg rounds. Our a pproach is to train a three-layer feed-forward network by using aGen eralized Probabilistic D escent (GPD ) learning rule. GP D is originally proposed byJuang [8] to train a speech classifier, which is reported to have a much betterrecognition performance than the well-known Back-Propagation (BP) [9] training.However, to our best knowledge, GPD is rarely or even never used by the computer-vision community. Because G PD is based o n minimizing a classification related errorfunction, it theoretically can produce a better classification performance than theclassifiers (such as BP ) based on minimizing a least-mean-square error. Furthermore,to make FR insensitive to the illumination variation, a novel light compensationH - Y . Shu m, M. Liao, and S - F . Chang (Eds.): PCM 2001, LNCS 2195, pp. 237-244, 2001.@ Springer-Verlag Berlin Heidelberg 2001H.-Y. Shum, M. Liao, and S.-F. Chang (Eds.): PCM 2001, LNCS 2195, pp. 237244, 2001.c Springer-Verlag Berlin Heidelberg 2001


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238 Y . 3 . H u a n g, Y .-H . Tsai, a n d J.-W. Shiehmethod is proposed to compensate the image variation resulted from the lightingfactor.This paper consists of five sections. Section 2 describes the used error functionand the derived GPD weights updating rule. Section 3 describes the proposed lightcompensation method which can reduce the image difference occurred from theillumination variation. Section 4 specifies the ITRI (Industrial Technology andResearch Institute) face database and its construction guidelines. Section 5 thenperforms several experiments and makes a performance comparison among LVQ2,BP, and GPD. Finally, Section 6 draws our conclusions and point out the futureresearch directions.

2 A Generalized Probabilistic Descent LearningThe key idea in GPD formulation is the incorporation of a smooth classification-related error function into the gradient search optimization objective. In general, GPDcan be applied to train various kinds of classifiers. Here, a three-layer feed-forwardnetwork is used to serve the classifier architecture.

Assume there are K persons in the concerned pattern domain, C, ,..., C, , and thefeature of an observed face is x = (x,,...,x, ) ,where n is the feature dimension. Letg, (x) be a discrimination function indicating the degree to which patternx belongs toperson i . In general, a pattern x is classified to be person M if g, ( x ) is thelargest value among { g , x ) 1< i < K} , that is M = argmaxg, (x) . In order toI


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Robust Face Recognition with Light Compensation 239

g, - g, is, the larger E ( x ) will be. This is a desired property because the smallvalue of g, -g indicates the poor classification ability of the trained classifier.Therefore, the defined E ( x ) is appropriate to serve as an m r unction.Consequently, to minimize E ( x ) corresponds to derive a better classifier.

A three-layer network is used to classify an input face panem, which consists of theinput, hidden, and output layers. I, denotes the i th node of the input layer whichcontains the value of the i th feature element of an input x , 0, is the output value ofthe j h node of the hidden layer, and 0, is the output value of the k th node of theoutput layer. In this network, gk means 0, , nd

1E ( x )= 4-0,1+ e '(")where n and m are the node numbers of the input and hidden layers respectively,

Wg is the connection weight between the i th node of the input layer and the j hnode of the hidden layer, and W,> s the connection weight between the j th node ofthe hidden layer and the k th node of the output layer. Therefore, to minimizeE ( x )corresponds to derive the optimized y7 nd Wjk Accordiig to the generalized deltarnle, W;i and W,%can be updated as follows:

where a(n)s a positive and monotonically-decreased learning rate. By using thechain rnle with simple mathematical operations, it is easily to derive



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240 Y . 3 . H u a n g, Y .-H . Tsai, a n d J.-W. Shieh3 Light compensation for Face ImagesIt is well known that the image colors (or gray levels) are very sensitive to the lightingvariation. A same object with different illumination may produce considerablydifferent color or gray-level images. The first row of Fig. 1 shows several face colorimages which are taken from different lighting conditions. In general, it is difficult toproduce a good classification accuracy if face samples in the training and testing setsare taken from d i f f m t lighting conditions. Therefore, a light compensationpreprocessing is essential, which can reduce the image difference resulted fromillumination variation to the minimum.

A novel light compensation method with a six-step processing procedure isproposed as follows:Step 1: transform each face image pixel F(m,n) from the RGB space tothe YC,C, space, where 1 Im I H and 1I n IV (H is the pixel width of the faceimage and V is the pixel height of the face image) , nd let P(m,n) be the gray level ofmvo.Step 2: mark each F(m,n) to be a skin-color pixel if it satisfies conditions defined in[lo].Step 3: approximate an equation Q(x,y) to all marked skin-color pixel's gray levelsP(m,n). Here, Q(x,y) is designed to be a second-rder equation. That is

P(x9 Y )=ecsY )= a x 2 + b V + c Y 2 + d x + e y +f.Parameters a, b, c, d, e, and f can be derived from the least-mean square error betweenP(m,n) and Q(m,n) for all marked skin-color pixels. The derived Q(x,y) indeedapproximates the background lighting distribution for the current face image.Step 4: subtract Q(m,n) from P(m,n) to derive a subtracted image ~ ' ( m ,) , hat is

Step 5: compute the average gray level p and the standard deviation d from themarked skin-color subtracted pixels. That is

where N is the total number of pixels marked as skin color.Step 6: normalize each pixel's gray level p 9 ( m , n ) o generate a new value by thefollowing equation

240 Y.-S. Huang, Y.-H. Tsai, and J.-W. Shieh


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where is a scale factor which purpose is to make the range of the transformed graylevels between 0 and 255. It is worthwhile to mention that the transformed faces have

an average gray level close to 128 and have similar gray-level standard deviations.Before applying the proposed light compensation method, the face portion of image

should be extracted from the whole image so that the skin-color-pixel detection can

focus only on the face image. Fig. 1 shows respectively some face images (first row),

skin-color-pixel images where white color denotes skin color and black color denotes

non-skin color (second row), background lighting distributions (third row), subtracted

images (fourth row), and normalized images (fifth row) by using the proposed light

compensation method. From inspection, the final normalized images have more

similar gray-level distributions so that a face classifier can learn the truly personal

distinct characteristics..

4 Face Database Construction

Because we aim to recognize faces under a loosely controlled surveillance

environment, it is important to consider many variation factors when collecting the

face database, which includes

(1) Face rotation : Each person was asked to look at 14 marked points attached on

walls, every two neighboring points approximately form a 10-degree viewing angle tothis person.

(2) Background : Pictures are taken at two diagonal corners of an office room. The

two corners look quite differently to each other; one is near windows which is a more

homogenous background, and the other is near a door with a cluttered desk.

(3) Illumination : Since one background is close to window, it accepts sort of day

light. Therefore, the taken images are brighter than those taken from the other

background. Also, because the day light is not always normal to the peoples faces,

some face images present one side brighter than the other side.

There are 46 persons involved in our face database whose pictures are display on Fig4. Each person was arranged to stand sequentially at two corners of one office room,

and at each corner he/she was asked to rotate his/her head for about every 10 degrees

by looking at 14 predefined wall marks. For each head rotation, a video camera (with

resolution 320*240) is used to take pictures. Therefore, there are 46*14 pictures in

total. Fig. 2 shows the taken images of one particular person.

5 Experiment Results

Currently, the face images are extracted from the original whole images manually. In

general, an extracted face image is the rectangular portion of image which contains

eyebrows, eyes, nose, and mouth. Since the statistically-based classification

approaches generally require a lot of training samples, it is important to increase the

sample number of database images. However, it is expensive to collect the real face

images. So, we increase the face images by two ways. The first one is to produce a


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Robust Face Recognition with Light Compensation 243AcknowledgementsThis work i s an o utco me o f t h e Ne two rk Ge n e ra t io n H u m d C o m p u te r Int er fa ceProject (pro ject code: 3XSlBI I ) supported by Ministry of Econom ic Administration(MOEA), Taiwan, R.O.C.ReferencesI . K.K. sung and T. Poggio, "Erample-Baed Learnlng for view-Baed Human Face

Detection." IEEE Tmns. Pan. Anal. Machine Intell.. Vol. 20, pp. 39-51. 1998.2. H. k Rowley, S. Baluja. and T. Kanade. "Newdl network-bawd face detection." IEEErransactions on PAMI.. vol. 20. no. I , pp. 22-38. Jan. 1998.3. D. M. Cavrila ."The visual analysis of human movement: a survey." Computer Vision andImageUnderstanding. vol. 73. pp. 82-98. 1999.4. M. Turk and A. Pmtland, '%ihenfaces for Recognition", J o ~ df CognitiveNeuroscience. March, 1991.5. R Brunelli and T. Poggio, "Face Recognition: Feamres Versus Templates", lEEE Trans.Pan. Anal. Machine Intell. Vol. 15. No. 10, October. pp 1042-1052. 1993.6. R. Chellappa, C. Wilson and S. Sirohey, "Ilunron and Machine Recognition of Faces: AS ~ u n w ' .mc. Of lEEE. Vol. 83, No. 5. May. pp 705-740, 1995.7. A.K. Jain, R. Bolle and S. Pankanti, Riometrics: Per.wnol ldenti/icafion in NemrkedSociety,Kluwer Academic Publishers, 1999.8. B.H. Juang and S. Karagiri. "Di.wriminotiw Leorning or Minimum Ermr Classi/ication",IEEE Trans. On Signal P n x s s in s . Vol. 40. No. 12, Ueccmber,pp 3043-3054. 1992.

9. B. Widrow and R. Winter. "Neuml Nets for Adoptive Filtering and Adaptive PonernRecognition", Computer. Vol. 21. No. 3. pp. 25-39, March. 1988.10. C. Garcia and G. Tziritas, '%ace Detection Using Quantized Skin Color Regiom Megingand wrrvelet Pocket Analysis". lEEE Trans. On Multimedia Vol. I No. 3, pp 264277,1999.'11. T. Kohonm "Theself-Oganizalion Map". Roc. OfIE EE , 78:1468-1680,1990.

Table I. The test recognition rates of three face class ifim withand without light compensation

I Approach I Recognition Rate I



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244 Y.-S . Huang, Y . - H . Tsai, an d JLW. Shieh

Figurc 1. This figure shows respectively some face images (first row), skin-color-pixel images (second row), background lighting distributions (thirdrow), subtracted images (fourth row), and normalized images (fifth row) b yusing the proposed light compensation m ethod.

Figure 2 Pictures taken from different illumination and head rotation angles bya digit camera, the image size of each picture is 320*240.

244 Y.-S. Huang, Y.-H. Tsai, and J.-W. Shieh

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