OSPP Face Recognition Using Meta-Heuristic Algorithm

Transcription

1 IOSR Journal of Computer Engneerng (IOSR-JCE) e-issn: ,p-ISSN: , Volume 19, Issue 3, Ver. III (May - June 2017), PP OSPP Face Recognton Usng Meta-Heurstc Algorthm Syed ArshRahaman 1 (Computer Scence and engneerng, BanuraUnnayan Insttute of Engneerng,Inda) Abstract :Face recognton has drawn dramatc attenton due to the advancement of pattern recognton technologes. Face recognton systems have reached a level of maturty under certan condtons but stll the performance of face recognton algorthms are easly affected by external and nternal varatons. Thus many well-nown algorthms have been proposed to overcome these challengng problems. Here we are tryng to use one sample face mage of ndvdual for tranng the whole system whch wll not only reduce labourng effort for the collecton and also reduce cost for storng and processng them. One sample per person face recognton (OSPP) s consdered as a challengng problem n face recognton communty and lac of samples leads to performance deteroraton. Here face recognton s performed by applcaton of the swarm optmzaton algorthms [12]. It was found out that the underlyng foragng prncple and the swarm optmzaton can be ntegrated nto evolutonary computatonal algorthms to provde a better search strategy for fndng optmal feature vectors for face recognton. Fnally, t s beleved that the partcle swarm optmzaton may be useful for the development of face recognton system. A meta-heurstc algorthm PSO s used that maes few or no assumptons about the problem beng optmzed and can search very large spaces of canddate solutons and also used for classfyng purpose. Keywords: One sample per person, partcle swarm optmzaton, meta-heurstc algorthm, face recognton, ntra-class varety model I. Introducton The fundamental tas of face recognton technology s to recognze a human face. Here we consder the mages (tranng set) of dfferent subject and then extract the features from them. the next step ncludes tranng the system for classfcaton purpose. The same archtecture s followed and one sample per person s consdered for the tranng purpose and for classfcaton purpose Meta-heurstc algorthm le PSO s used. Partcle Swarm Optmsaton s an algorthm capable of optmsng a non- lnear and multdmensonal problem whch usually reaches good solutons effcently whle requrng mnmal parametersaton. The basc concept of the algorthm s to create a swarm of partcles whch could move n the space around the problem space searchng for ther goal, the place whch best suts ther needs gven by a ftness functon. OSPP face recognton algorthm s based on PSO. If we consder a neutral mage of each subject as a partcle, each partcle has a specfc mode of drecton (mode of drecton has been dscus n proposed model). In each teraton every partcle moves towards optmal soluton based on the cost functon. after a specfc amount of teratons, the partcle holdng best cost functon wll be the optmal pont (soluton). Partcle swarm optmsaton s chosen to fnd out the optmum combnaton of the bass and varety n terms of the mnmum L2 dstance relatve to the query mage. The partcles are scattered randomly n the search space. As they search for the optmum value, the partcles balance the objectves of followng the value gradent and random exploraton. Over tme they begn to congregate n the general area of the optmum value. Fnally, the partcles converge to the optmum value n the concept. Experments on the extended Yale face database are provded to show the valdty of the proposed algorthm. II. Materals and methods OSPP Problems Accuracy s generally proportonal to the number of tranng samples collected for face recognton. The one sample per person (OSPP) problem focuses on dentfyng a person usng only one tranng sample. The facal recognton from a sngle sample reduces the labour requred to gather tranng data and enables some applcatons where only a sngle sample wll be avalable. Based on the features used n OSPP, the problem can be classfed nto three part Holstc methods: These methods dentfy a face usng the whole face mage as an nput. The man challenge faced by these methods s how to address the extremely small sample problem. such as Extensons of prncpalcomponent analyss (PCA) enrched face mages wth ts projectons. ( PC) 2 A: Two-dmensonal PCA [2] Local methods: These methods use the local facal features for recognton. Care should be taen when decdng how to ncorporate global confrontatonal nformaton nto local face model. Such as DCP Use DOI: / Page

2 OSPP Face Recognton Usng Meta-Heurstc Algorthm drectonal corner ponts (DCP) features for recognton Graph matchng methods [3-6] Local probablstc subspace Local probablstc subspace method Neural networ method SOM learnng based recognton Hdden Marov Model HMM method Hybrd methods: These methods use both the local and holstc features to recognze a face. These methods have the potental to o er better performance than ndvdual holstc or local methods, snce more comprehensve nformaton could be utlsed. Such as Vrtual samples + local features Local probablstc subspace method Partcle Swarm Optmsaton Partcle Swarm Optmzaton belongs to the feld of Swarm Intellgence and Collectve Intellgence and s a sub-feld of Computatonal Intellgence. The goal of the algorthm s to have all the partcles locate the optma n a mult-dmensonal hyper-volume. Ths s acheved by assgnng ntally random postons to all partcles n the space and small ntal random veloctes. The algorthm s executed le a smulaton, advancng the poston of each partcle n turn based on ts velocty, the best nown global poston n the problem space and the best poston nown to a partcle. The objectve functon s sampled after each poston update. Over tme, through a combnaton of exploraton and explotaton of nown good postons n the search space, the partcles cluster or converge together around an optma, or several optma. General Equaton of PSO If the partcles are n.m-dmensonal space then the t partcle can be represented as X = X 1, X 2.. X M and the poston of the partcles can be represented asp = P 1, P 2.. P M. now theposton of all partcle s P g,t (Global Best) and the best Poston of t partcle s P g,t (Personal Best). Where t s the teraton no. the rate of Poston change of t partcle s noted as V = V 1, V 2.. V M The poston change of t partcle can Modfed accordng to V,t+1 = w V,t + C 1 rand X t+! P g,t X t + C 2 rand (P g,t X t ) and = X t + V,t+1 wherec 1, C 2 are postve constant. and rand() s a random functon. OSPP Face Recognton Usng PSO One sample per person face recognton on Partcle swarm optmsaton was ntroduced by Yan Zhang and Hua Peng. The Bass plus varety model [11] was also ntroduced n OSPP-PSO.Neutral mages of each subject were consdered as partcles and the rate of poston changes of each partcles were ntated wth ntra class varety. K no of subjects were taen nto consderaton and each sample holds M no of samples and 1 neutral mage X 1 j, j = 1,2,3. Here for subject j, j = 1,2,3., and r,th sample r = 1,2,3. M.to ntate movement rate of each partcle (Neutral Image )the ntra-class varaton was used. Thentra-class varaton was once expressed as V = V 1, V 2 V 3 V m 1 = [! V1.]...(1) =1 V 2 V 1.! =1 V r V 1.! =1 V M Proposed Model To ntate rate of poston changes of each partcle we have used dfference of ntra-class average face and neutral face of same class nstead of ntra-class varaton. V = (X 1 1 M M n=1 X n ) = [ Neutral face Average face ] = 1,2,3., and X 1 s the neutral mage for class DOI: / Page

3 OSPP Face Recognton Usng Meta-Heurstc Algorthm System flowchart of Proposed Model Algorthm: OSPP usng PSO In OSPP-PSO, Neutral mage of each subjects are consdered as Partcles, and V = [ Neutral face Average face ], are the rate of poston change of partcles. Optmum soluton s the mnmum L2 dstance of query mage wth partcles (Neutral mage). The Optmum soluton can be acheved after a certan number of teratons n PSO. Step1: nput : Rate of Poston Change of Each PartclesV IR m.neutral Images X 1 IR m, = 1,2,3. K, K sfor Subjects, and Query mage q IR m and teraton t = 1 Step2: ntate the neutral mage as partcles for each subjects X = X 1 1, X X 1 Step3: Poston change of each partcle n frst teraton V,1 = V r r s the random nteger range between 1 to, here represent the subject and 1 s for frst teraton Step4: Evaluate the L2 dstance D t of subjects wth quarry mage q where = 1 tok.ntalze the L2 dstance of th partcle D g,t and mnmum dstance of whole Partcle D g,t D t = q X t 2, = 1,2,3.. (3) D g,t = nf; f t = 1, ten D g,t = D t ; Step5: Update the best Pont for Partcle, P g,t and update best pont among all the partcle pontp g,t such as f D t < D g,t,t then D g,t = D t andp g,t = P t...(4) D t < D g,t then D g,t = D t andp g,t = P t...(5) Step6: change the velocty and Poston for partcle usng V,t+1 = w V,t + C 1 rand P g,t X t + C 2 rand (P g,t X t ).(7) = X t + V,t+1...(8) X t+! wherec 1, C 2 are postve constant. and rand() s a random functon. Step7: t = t + 1 and go to Step:5 after maxmum number of teraton partcale wll get optmum ftness Step8:Output: Identty(q) = Identty(P g,t ) III. Results & Dscusson The algorthm has been tested wth the extended Yale face database B. The Yale face database B ncludes 39 subjects [YealB01 to YealB39] where each subject ncludes 64 pctures. Each sample pcture s separated nto two categores where 10 of the pctures are used for tranng purpose and the rest for testng purpose. yealb_p00a+00e+00 was taen as neutral mage for each subject. All samples were down sampled as [24, 21] and staced row wse.in the experment,number of teratons was set to 100. The experments were performed wth 4 subjects and 10 test samples each. It was done usng both the ntra- class varety model and the proposed model wth 10 test samples for each subject. Out of 40 attempts the ntra-class varety model gave 36 accurate results whch means that the model has 90% accuracy. In the proposed model out of 40 attempt 32 was found accurate whch mples that out proposed model has 80% accuracy. DOI: / Page

4 OSPP Face Recognton Usng Meta-Heurstc Algorthm FIGURES AND TABLES Here Result (A) shows the the performance of the proposed Model and Result(B) shows the performance of ntra Class varaton Model Fg: Performance Graph for PSO n OSPP DOI: / Page

5 OSPP Face Recognton Usng Meta-Heurstc Algorthm IV. Concluson A new OSPP face recognton algorthm s thus proposed. As ts been already sad that One sample per person face recognton (OSPP) s consdered as a challengng problem n face recognton communty and lac of samples leads to performance deteroraton. Here face recognton s performed by applcaton of the swarm optmzaton algorthms. To fnd the optmum pont n terms of mnmum L2 dstance PSO s selected wth the query mage. Partcle Swarm Optmzaton s used for optmally controllng the non-lnear systems. It s notably easy to program and mplement usng logcal and mathematcal operatons. PSO s one of the meta-heurstc algorthm that has been found to be robust n solvng non-lnear optmzaton problems. It s capable of generatng hgh qualty solutons wth more stable convergence characterstcs and shorter calculaton tme whle compared to the others. PSO s performance remans robust even when appled to relatvely hgh dmensons as typcally found n control applcatons. The dentty of the query mage s equal to the dentty of the bass mage of the optmum combnaton. One sample face mage of ndvdual s been used for tranng the whole system whch wll both reduce labourng effort for the collecton and also reduce cost for storng and processng. Acnowledgement I tae ths momentous opportunty to express our heartfull grattude and regards to vulnerable and hghly esteemed gude Mr. Pryaranjan Mshra. I am thanful to our colleagues whoprovded expertse that greatly asssted the research, although they may not agree wth all of the nterpretatons provded n ths paper. I also express out apprecaton to my dearest frend for sharng ther pearls of wsdom wth us durng the course. Nobody has been more mportant to me n the pursut of ths project than the members of my famly. I would le to than my parents, whose love and gudance are wth me n whatever I pursue. They are the ultmate role models. References [1] J. Wu, Z.-H. Zhou, Face recognton wth one tranng mage per person, Pattern Recognton Lett. 23 (14) (2002) [2] J. Yang, D. Zhang, A.F. Frang, J. Yang, Two-dmensonal PCA: a new approach to appearance-based face representaton and recognton, IEEE Trans. Pattern Anal. Mach. Intell. 26 (1) (2004) [3] B.S.Manjunath, R. Chellappa, C.V.D. Malsburg, A feature based approach to face recognton, n: Proceedngs, IEEE Conference on Computer Vson and Pattern Recognton, vol. 1, 1992, pp [4] M. Lades, J. Vorbruggen, J. Buhmann, J. Lange, Malsburg von der, R. Wurtz, Dstorton nvarant object recognton n the dynamc ln archtecture, IEEE Trans. Comput. 42 (3) (1993) [5] L.Wsott, R. Fellous, N. Kruger, C. von Malsburg, Face recognton by elastc bunch graph matchng, IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (July 1997) [6] B.Kepenec, F.B. Te, G. BozdagAar, Occluded face recognton based on Gabor wavelets, ICIP 2002, September 2002, Rochester, NY, MP-P3.10. [7] H.-S. Le, H. L, Recognzng frontal face mages usng hdden Marov models wth one tranng mage per person, Proceedngs of the 17th Internatonal Conference on Pattern Recognton (ICPR04), vol. 1, 2004, pp [8] A.M. Martnez, Recognzng mprecsely localzed, partally occluded, and ex- presson varant faces from a sngle sample per class, IEEE Trans. Pattern Anal. Mach. Intell. 25 (6) (2002) [9] S. Lawrence, C.L. Gles, A. Tso, A. Bac, Face recognton: a convolutonal neural-networ approach, IEEE Trans. Neural Networs 8 (1) (1997) [10] X. Tan, S.C. Chen, Z.-H. Zhou, F. Zhang, Recognzng partally occluded, ex- presson varant faces from sngle tranng mage per person wth SOM and soft NN ensemble, IEEE Trans. Neural Networs 16 (4) (2005) [11] YanZhang,HuaPeng,One sample per person face Recognton Usng PSO,IET sgnal processng,pp 1-6,2015. [12] James Kennedy, and Russel Eberhart (1995), Partcle Swarm Optmzeton,IEEE DOI: / Page