FACE RECOGNITION BASED ON EIGENFACES AND FUZZY ARTMAP NEURAL NETWORK

Transcription

1 The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium (ISITS 07), Oct 30-31, 2007, ITMA -UPM, Mlysi. FACE RECOGNITION BASED ON EIGENFACES AND FUZZY ARTMAP NEURAL NETWORK mikliz Abdul Krim 1, Shhrin Azun Nzeer 2, Mt Kmil Awng 3, Nzrudin Omr 4, Rubiyh Yusof 4 1,2,3,4 Telekom Reserch & Development Sdn Bhd Leboh Silikon Ide Tower, UPM-MTDC, Serdng, Selngor, Mlysi 5 Centre for Artificil Intelligence nd Robotics (CAIRO) Universiti Teknologi Mlysi, ln Semrk, Kul Lumpur, Mlysi 1,2,3,4 { jmikliz,shhrin,kmil,nzr }@tmrnd.com.my, 5 rubiyh@utmkl.utm.my Abstrct This pper presented fce recognition system using fuzzy rtmp s clssifier. The motivtion of using Fuzzy Artmp is becuse its offers unique solution to the stbility-plsticity dilemm i.e bility to preserves previously lerned knowledge (stbility) nd potentil to dpt new ptterns indefinitely (plsticity). The pper will initilly describe the methodology used for preprocessing, feture extrction, nd clssifiction. Experiment ws conducted using locl dtset which consist 10 subjects with 10 fce imge per subject. All imges were resized into size of 50 by 50 nd pply homomorphic preprocessing over them. Principle component nlysis is used s fetures extrction. Finlly, Fuzzy Artmp lgorithm is pplied in clssifiction lyer. Recognition rte obtined from experiment is 87.5%. As benchmrk, the sme dtset is used to trin using neurl network. The result chieved from neurl network is 95.0% Keywords: fuzzy rtmp, homomorphic, PCA, eigenfces 1. Introduction Automtic fce recognition hs become interest of reserch recently. Mny pproches hve been introduced in order to develop this system due to its potentil in security, surveillnces, low enforcement nd ccess control. The fce recognition problems which re due to illumintion, pose, occlusion, shdow, nd blurring re tckled using vrious methods such s geometricl feture bsed methods, holistic methods, templte mtching methods, grph mtching method, nd neurl network pproch [1,2,3,4,5]. In [1, 2, 5], principl component nlysis (PCA) is used to find the spects of fce which re importnt for identifiction. Then, neurl network is pplied to crete the fce dtbse nd recognized the fce. In [5], A.H. Boulleg proposed hybrid neurl network nd PCA by using geometricl pproch s preliminry clssifiction of fce. Fuzzy rtmp is supervised lerning which motivted by [6]. Fuzzy rtmp is well known pttern clssifiction scheme tht cpble of self-orgnising stble recognition ctegory. It offers unique solution to the stbility-plsticity dilemm, i.e how lerning system cn sfely dpt to novel informtion without corrupting or forgetting previously lerned informtion [20]. Thus, fuzzy Artmp is network tht cn operte in both offline nd online modes. Besides, it s overcome the problem of long trining nd ctstrophic forgetting ssocited unlike mny populr networks such s bck propgtion neurl network [7]. Becuse of this, Fuzzy Artmp hs been used in mny res of pttern recognition [14, 15, 16, 17, 18, 19]. This pper presented hybrid method of PCA nd fuzzy rtmp for clssifiction bsed on our locl dtset. The im of this pper is to evlute performnce of Fuzzy rtmp on fce pttern clssifiction. The remining of this pper is orgnized s follows. In section 2 described the fce recognition system nd its components. In section 3, the experimentl design nd result is presented, nd the conclusions of this work re given in Section 4. 27

2 ISITS 07, The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium, Oct 30-31, 2007, ITMA, UPM, Mlysi. 2. Fce Recognition System Fig. 1 shows the block digrm of the proposed fce recognition which is divided into two (2) phses, which re the trining nd testing phses. Ech phse consists of three min modules, which re the preprocessing module, feture extrction module, nd clssifiction module. Trining Phse Normlized fce imge Testing Phse Normlized fce imge from fce Pre-Processing Pre-Processing Feture extrction Imge Projection Trining Clssifier Biometric Templte Output results 2.1. Dtset Dtset imges were collected mong TMR&D stff. There is 10 stff tht contributed 10 imges per stff. The imges re well-ligned frontl view. Figure 2 shows the smple of dtset collection. () (b) (c)!! 2.2. Homomorphic Filtering Imge normlly consists of light reflected from object. The bsic nture of the imge my be chrcterized by two (2) components, which re illumintion nd reflectnce. Illumintion is the mount of source light incident on the scene being viewed. Menwhile, reflectnce is the mount of light reflected by the objects in the scene. The preprocessing is used to reduce or eliminte some of the vritions in fce imge due to illumintion. It normlized nd enhnced the fce imge to improve the recognition performnce of the system. By performing explicit normliztion processes, system robustness ginst scling, posture, 28

3 The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium (ISITS 07), Oct 30-31, 2007, ITMA -UPM, Mlysi. fcil expression nd illumintion is incresed. The technique pplied for preprocessing is homomorphic filtering Principle Component Anlysis The feture extrction is used to reduce the dimension of the fce spce by trnsforming it into feture representtion. It will be responsible for trnsforming or composing the normlized or pixel vlues of the fce imge nd represents it into n pproprite representtion or feture vector by finding the key fetures tht will be used for clssifiction. The method used for the proposed fce recognition is eigenfces. Eigenfces method trnsforms fce imges into smll set of chrcteristics feture imges clled eigenfces, which re the principl components of the initil trining set of fce imges [8]. It used more informtion by clssifying fce bsed on generl fcil pttern. These ptterns include fetures of the fce nd other informtion. Eigenfces method is currently the common nd most effective method for fce recognition. It is ble to recognized humn fce by compring fcil structure to tht of known person. The system will project the imge into fce spce. Fce spce is defined by eigenfces nd eigenvector of the set of imges. In fce recognition, eye, nose, mouth in ny fce s well s reltive distnce between these object is clled chrcteristic fetures. Trining imges re stndrdized with respect to size, orienttion nd lighting density. Ech fce imge is converted into column vector. After tht, these imges re combined to crete n imges vector of size k by n where n is the number of fce imges nd k is the number of pixel. The verge of the whole fce imges is clculted to crete men centered imges. The covrince mtrix hs up to n eigenvector ssocited with non-zero eigenvlues. Eigenfces re rnked in eigenvlues decresing order. The eigenvectors corresponding to the lrgest eigenvlues re the most significnt thn those with smller eigenvlues. To reduce dimensionlity, only projections over the p eigenvectors (p < k, number of pixel of input imges) corresponding to the p gretest eigenvlues re considered [9]. The detiled concept nd explntion on eigenfces cn be found in [10] Fuzzy Artmp A FAM consists of pir of fuzzy ART modules (refer to fig 2), ART nd ART b, connected by n inter-art module clled Mpfield. Detils cn be found in [11,12]. The fuzzy module contins the input lyer F 1 nd the competitive lyer F 2. A preprocessing lyer is lso dded before F 1. The following nottions pply: M is the number of nodes in F 1, N is the number of nodes in F 2, nd W is the weight vector between F 1 nd F 2. Anlogous lyers nd nottions pper in ART b. The Mpfield module contins lyer F b. The number of nodes in F b is equl to the number of nodes in F b 2. Ech node j from F 2 is linked to ech node from F b 2 vi weight vector W b j, where W b j is the j th row of W b, j=1,..,n. All weights re initilized to one.we sy tht node (lso clled ctegory) from F 2 is uncommitted if it hs not lerned yet n input pttern, but committed otherwise. " #!! $ # Before presenting the FAM lgorithm, there is some specifiction should be mde [13]: 29

4 ISITS 07, The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium, Oct 30-31, 2007, ITMA, UPM, Mlysi. 1. All input vectors re complement coded by F 0 lyer. Ech input vector = (, n ) produce normlized vector A=(,1-). So, M = 2 n 2. The opertor is the fuzzy AND opertor nd is defined s (p q) = min (p i, q i ) where p= (p i, p n ) nd q = (q i, q n ) 3.. is the L 1 norm defined by n p = pi i= 1 The lerning lgorithm for FAM is described below [13]: 1) Set the vigilnce prmeter fctor equl to its bseline vlue ρ = ρ nd consider tht ll nodes re not inhibited (i.e., every node prticiptes in looking for n pproprite ctegory for the current input pttern). 2) For ech (preprocessed) input A, fuzzy choice function is used to get the response for ech F 2 ctegory w j T ( A), j = 1,..., j + w j A = N (1) α 3) Let be the node index with the highest vlue computed in (1) = ind T (2) mx j=1,... N { j } 4) Check the resonnce condition. If the input is similr enough to the winner s prototype, q A w ρ (3) A If this condition is fulfilled, go to step 5, otherwise, inhibit the th node such tht it will not prticipte to further competitions for this pttern. If there exist noninhibited nodes, then go to step 3); else, crete new ctegory to represent the input vector (nd let be the index of the newly dded node). 5) Let K be the winning node from ART b. The F b 2 output vector is set to 1, if k = K y b = k = 1,...N b (4) k 0, otherwise b An output vector X is formed in mpfield b b b X = y w (5) 6) A Mpfield vigilnce test controls the mtch between the predicted vector b vector y Where ρ [ 0,1] b X y j b ρ (6) b b 30 b X nd the trget is Mpfield vigilnce prmeter. If the test from (6) is pssed, then lerning occurs in ART, nd Mpfield [step 7)]. Otherwise, mtch trcking is initited [step 8)]. 7) A lerning process occurs in the fuzzy ART modules nd in Mpfield w ( new) nd = β ( A w ) + (1 β ) w (7) ( old) ( old)

5 The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium (ISITS 07), Oct 30-31, 2007, ITMA -UPM, Mlysi. w b k 1, if k = K = 0, if k K (8) Go to step 9. 8) Mtch trcking: increse ρ ρ = q A w + (9) A 9) If more ptterns to be lerned go to step 1, else STOP. For clssifiction problem, i.e the problem for which the totl number of clsses is known the priori, the clss index is the sme s the ctegory number in F b 2 ; thus, ART b cn be simply substituted by n N b dimension vector [13]. In testing phse, when n input is presented to ART module, w vector of the chosen ctegory in F 2 determines the clss of the input. Performnce evlution of this system is bsed on recognition rte, flse cceptnce rte (FAR), flse rejection rte (FRR) nd totl error rte. The error rte is clculted by eqution Experimentl Design 3.1 Fce Dtset Totl error rte = (FRR + FAR)/2 (10) The locl fce dtset consists of 100 fce imges of Mlysin. There re 10 different imges of 10 distinct subjects. The imges re well-ligned frontl view, nd re divided into two prts: 6 imges per subject re used for trining nd nother 4 imges per subject re used for testing with totl of 60 trining, imges, nd 40 testing imges. For the experiment, the imges re rescled to resolution of 50 by 50 to be process in the system. 3.2 Experiment Ech imge is converted into imge vector. The imge vectors re then pre-process using homomorphic filtering to reduce the illumintion problems occurred during dtbse collection. The pre-processed imge will trnsform into eigenspce using Principle component Anlysis (PCA). In this stge, the dimension of the imges is reduced. Only importnt fetures re selected to be trined by fuzzy rtmp. b There re three importnt prmeters tht control the FAM dynmics [6]: choice prmeter, ; lerning prmeter, ; nd vigilnce prmeter, ρ. Thus, for this experiment, Fuzzy rtmp rchitectures re design s in tble 1. % #!! $ Prmeters Vlues 0.01 ρ 0.95 Before output of PCA is feed into fuzzy rtmp, ech fce set is normlized into vlue of rnge in 0-1. Inputs re then been complement s discussed in specifiction 1. Ech input to be feed in fuzzy rtmp module will ssocited with desired output vlue. This output vlue will represented clss index which generted n N b dimension of ART b. Therefore, the system will lern to mp the ssocited input-output pirs (fst lerning)

6 ISITS 07, The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium, Oct 30-31, 2007, ITMA, UPM, Mlysi. Block digrm for experiment conducted is shown in fig 4 nd process flow to crete fetures vector is shown in fig 5. Input vector Pre-processing Fetures Extrction Clssifier: FAM Decision & ' #! 32 ( ) * # + Two experiments hve been conducted nmely experiment A nd B. The purpose of experiment A is to investigte the effect of vrying ρ vlues on recognition rte. While the others prmeters is remined. From experiment A, by using ρ vlue which gives the highest recognition rte, Experiment B is conducted. The objective of this experiment is to investigte the performnce of the system by vrying number of trining set. 3.3 Result Tble 2 represents result for experiment A. % + # ρ ρ Recognition rte (%) From tble 2, the highest recognition rte obtined is 87.5% with ρ is By using this vlue, experiment B is conducted nd the result is shown in tble 3. % " #

7 The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium (ISITS 07), Oct 30-31, 2007, ITMA -UPM, Mlysi. # trining imges Recognition rte (%) From tble 3, by incresing number of trining set, the better recognition rte is obtined. From the experiment, FAR nd FRR is 0.67 nd 6.0 respectively. As benchmrk, the sme trining set is used to trin using neurl network. Tble 4 shows the comprison bsed on recognition rte. % & Clssifier Recognition rte (%) Fuzzy Artmp 87.5 Neurl 95.0 Network 4. Conclusion The min objective of this work ws to evlute the performnce of Fuzzy rtmp on fce pttern clssifiction. In order to understnd fuzzy rtmp rchitecture, two experiments were crried out. Experiment A is crried out to investigte the effect of vigilnce vlues to the system performnce. From this experiment, the pick performnce is obtined t ρ = 0.95 with 87.5% recognition rte. Experiment B, is crried out to investigte the performnce of the system by vrying number of trining set. From the experiment, the bigger size of trining set contributed the higher recognition rte. In conclusion, we cn sy tht, recognition for locl dtset is 87.5%. Flse cceptnce rte (FAR), flse rejection rte (FRR) nd totl error rte re 0.67%, 6.0% nd 3.335% respectively. In the future, we intend to further our reserch on lrger dtset nd to investigte the cpbility of fuzzy rtmp during online version. 5. Acknowledgement The uthors wish to thnk TM Reserch nd Development Sdn. Bhd. for their funding support under project R nd R References [1] Nzeer, S. A., Omr, N., Krim,. A., Khlid, M., Yusof, R., Fce Recognition Using Eigenfces nd Multilyer Perceptron Neurl Network. Konferensi Nsionl Sistem Informsi (KNSI), Bndung. [2] R.Q. Feitos, M.M.B. Vellsco, D.T Oliveir, D.V. Andrde, S.A.R.S. Mffr, Fcil Expression using RBF nd Bck Propgtion Neurl Networks, Ctholic University of Rio de neiro, Brzil. [3] Qing ing., Principle Component Anlysis nd Neurl Network Bsed Fce Recognition, University of Chicgo. [4] mil N, Iqbl S, Iqbl N, 2001, Fce Recognition using Neurl Network, Multi Topic Conference (IEEE INMIC 2001), Technology for the 21st Century. Proceedings, pp [5] Boulleg, A.H.; Bencheriet, Ch.; Tebbikh, H., 2006, Automtic Fce recognition using neurl network-pca, Informtion nd Communiction Technologies (ICTTA) 2006,2 nd Vol. 1, pp [6] G. Crpenter, S. Grossberg, N. Mrkuzon,. Reynolds, nd D. Rosen, Fuzzy ARTMAP: neurl network rchitecture for incrementl supervised lerning of nlog multidimensionl mps, IEEE Trns. Neurl Network., vol. 3, no. 5, pp , Sep

8 ISITS 07, The 2 nd Ntionl Intelligent Systems And Informtion Technology Symposium, Oct 30-31, 2007, ITMA, UPM, Mlysi. [7] G. Brtfi, An Improved Lerning Algorithm for Fuzzy Artmp Neurl Network, Technicl Report CS-TR- 95/10, Deprtment of Computer Science, Victori University of Wellington, New Zelnd. [8] M.A. Turk nd A.P. Pentlnd., 1991, Fce Recognition using Eigenfcess, In IEEE Proc. Of Computer Vision nd Pttern Recognition, pp [9] R.Q Feitos, C.E Thomz, A. Veig, 1999, Compring the Performnce of the Discriminnt Anlysis nd RBF Neurl Network for Fce Recognition, Interntionl Conference on Informtion System Anlysis nd Synthesis, Orlndo. [10].Shlens, 2003, A Tutoril on Principle Component Anlysis: Derivtion, Discussion nd Singulr Vlue Decomposition, version 1. [11] Fusett, Lurene V., 1994, Fundmentls of neurl networks: rchitecture, lgorithms, nd ppliction, Prentice-Hll, Inc. [12] C.H Chen, 1996, Fuzzy Logic nd Neurl Network Hndbook, McGrw-Hill. [13] R zvn Andonie, Lucin Ssu, Fuzzy ARTMAP with Input Relevnces, IEEE Trnsctions on Neurl Networks, Vol. 17, No. 4, ULY [14] Ty, Y. H., Khlid, M., Tn, K. K., & Yusof, R., Hnd-Written Postcode Recognition by Fuzzy ARTMAP Neurl Network, COSTAM Ntionl Science Congress, Genting Highlnds, November, [15] M. Busque, M.Prizeu, A comprison of Fuzzy Artmp nd Multilyer Perseptron for Hndwritten Digit Recognition, Computer vision nd System Lbortory, University Lvl, Cnd, [16]. Downs, R.F Hrrison, R.L Kennedy, S.S Cross, Appliction of fuzzy Artmp neurl network model to medicl pttern clssifiction tsks, Artificil Intelligence in Medicine 8 (1996), pg: , Elsevier Science B.V [17] Murshed, N.A.; Bortolozzi, F.; Sbourin, R, Offline Signture Verifiction using Fuzzy Artmp Neurl Network, Neurl Networks, Proceedings, IEEE Interntionl Conference Volume 4, 27 Nov.-1 Dec Pge(s): vol.4 [18] Siew Chn Woo; Chee Peng Lim; Osmn, R., Text-dependent speker recognition using the fuzzy ARTMAP neurl network, TENCON Proceedings Volume: Pge(s): vol.1 [19] Amin, A.; Murshed, N., Recognition of printed Arbic words with fuzzy ARTMAP neurl network, Neurl Networks, ICNN '99. Interntionl oint Conference. Volume: 4, 1999 Pge(s): vol.4 [20] Mei Ming Kun; Chee Peng Lim; Mord, N.; Hrrison, R.F.;, An Experimentl Study of Originl nd Ordered Fuzzy Artmp Neurl Network in Pttern Clssifiction Tsks, TENCON Proceedings Volume 2, Sept Pge(s): vol.2 34