Joint Feature Learning With Robust Local Ternary Pattern for Face Recognition

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1 Join Feaure Learning Wih Robus Local Ternary Paern for Face Recogniion Yuvaraju.M 1, Shalini.S 1 Assisan Professor, Deparmen of Elecrical and Elecronics Engineering, Anna Universiy Regional Campus, Coimbaore, Tamil Nadu, India Pg Scholar, Deparmen of Elecrical and Elecronics Engineering, Anna Universiy Regional Campus, Coimbaore, Tamil Nadu, India ABSTRACT In he age of rising crime, here is a criical need for high securiy. Biomerics has now gained a lo of aenion. Facial biomerics is a mehod ha can idenify a specific individual in a facial image by analyzing and comparing paerns. Hence posiion-specific discriminaive informaion can be exploied for face represenaion using Join feaure learning mehod. Having learned hese feaure projecions for differen face regions, spaial pooling is performed for face paches wihin each region o enhance he represenaive power of he learned feaures. Moreover, JFL model is sacked ino a deep archiecure o exploi hierarchical informaion for feaure exracion and i furher improves he recogniion performance. In his paper, he exension of LBP algorihm is proposed. LBP is normally sensiive o noise and Local Ternary paern parially solves his problem by encoding he minimum pixel difference ino a separae sae. The minimal pixel difference may be easily overwhelmed by noise. Thus, i is difficul o accuraely deermine is sign and magniude. In his paper, concep of uncerain sae is inroduced o encode he small pixel difference. Robus Local Ternary Paern is combined wih JFL for beer feaure exracion and o improve he robusness o image noise. Keywords:- Face recogniion, Local Ternary paern, Feaure exracion, Pixel difference 1. INTRODUCTION Facial biomerics recogniion sysem is a compuer based digial echnology and is an acive area of research nowadays. I has various applicaions like auhenicaion sysems, securiy sysems and searching of persons. These applicaions are cos effecive and i reduces ime consumpion. In pas few years, several face recogniion echniques are proposed wih varied algorihms and are implemened successfully. The abiliy of raining and idenifying he objec images are convered ino machine sysems using he Arificial Neural Neworks. The funcion of he facial image recogniion sysem is o compare he person s face which is o be recognized wih he faces already rained in he Arificial Neural Neworks.The bes maching face as oupu even a differen lighing condiions, viewing condiions and facial expressions are considered for maching daabase. Rapid developmen of face recogniion is due o acive developmen of algorihms, large image daabase and he process of evaluaing he performance recogniion algorihms [1], []. Hence Facial Recogniion Technology (FRT) has emerged as an easy soluion o address many requiremens for idenificaion [1] and verificaion of ideniy claims. Smarphone face represenaion capaciies include objec agging, social neworking inegraion purposes and personalized markeing. Facial image represenaion schemes can be divided ino local and global. Mos global feaure learning approaches are based on Principal componens represenaion of he facial image inensiies. This represenaion scheme was brough firs for objec image compression and also used for recogniion purposes. Laer his was denoed as eigenfaces for his ype of represenaion. Human face is represened as a vecor of inensiies and his is hen approximaed as a sum of basis vecors. Recogniion algorihms can be mainly divided ino geomeric and phoomeric. Geomeric approach looks a he disinguishing feaures whereas phoomeric is a saisical approach ha changes an image ino values and his value is used o compare wih emplaes so ha variances are eliminaed. Recen Face recogniion algorihms include PCA using eigenfaces, LDA, Elasic Bunch Graph maching and Mulilinear Subspace learning using ensor represenaion ec. Figure 1 Face recogniion Block diagram Volume 5, Issue 6, June 016 Page 11

2 The following mehods are he basic approaches for face recogniion 1) Feaure represenaion approach In his approach, local feaures like nose and eye are segmened and can be used as inpu daa in face recogniion. ) Holisic approach In case of Holisic approach, he whole face is considered as daa inpu. 3) Hybrid approach I is he combined approach of feaure based and holisic. Boh local and whole face is used as an inpu o face deecion sysem. 1.1 Differen Exising Approaches Principal Componen Analysis Figure Face recogniion algorihm classificaions Eigenface is a pracical approach for face recogniion and i is very easy o implemen. I helps in efficien processing of ime and sorage. PCA helps in reducing he dimension size of an image in a minimal period of ime [14]. There is a high correlaion beween he raining daa and he daa ha is recognized. The accuracy of his algorihm depends on many hings. Considering he pixel value as prominen feaure used for comparison in case of feaure projecion, he accuracy would decline wih changing ligh inensiy. Preprocessing of image is a mus o achieve desired resul. The advanage of his algorihm is ha he eigenfaces were used exacly for he purpose which makes he sysem very efficien. The size and locaion of each face image should remain similar which is he main limiaion of his algorihm Independen Componen Analysis Independen Componen Analysis is similar o PCA algorihm. The only difference is ha he disribuion of he componens are said o be non-gaussian. This analysis separaes he higher order momens of he inpu. Boh he algorihms lead o a similar performance. ICA represenaions are designed o increase he informaion ransmission in he presence of noise. Due o his feaure, hey are robus o few variaions such as lighing condiions and facial expression. This can be considered as forms of noise wih respec o he main source of informaion. The facial recogniion analyzed during differen days is highly recommended because mos applicaions of face recogniion conain he noise inheren in evaluaing he images colleced on a differen day from he sample images. The disadvanages of Fisherface are ha i is more complex han Eigenface in finding he projecion of face space. As beer classificaion is aken ino accoun, he dimensional projecion in face space is no as compac as Eigenface which resuls in increasing sorage of he face and more processing ime in recogniion Elasic Graph Maching The general process is o compare graphs wih images and based on his new graphs are generaed. A labeled graph has a se of jes which are arranged in a provided spaial order. The image jes also have he same spaial arrangemen which is available in he graph jes and each image je relaes o one graph je [14]. In conras o PCA using eigenface mehod, he elasic bunch graph maching echnique rea one vecor per feaure of faces. The advanage of his mehod Volume 5, Issue 6, June 016 Page 1

3 is ha any change or missing of any one feaure does no mean ha he person will no ge recognized. When a new face image is added, no addiional effor is needed o modify emplaes because i is already sored. I is made possible o recognize a person up o roaion of degrees. I is very sensiive o lighing condiions and also several graphs have o be placed manually on he face. When he changes in lighing are high, he resuling feaure exracion will have a significan decrease in he recogniion rae.. RELATED WORK In his secion, hree algorihms relaed o feaure exracion are discussed..1 Join Feaure Learning Supervised face recogniion sysems uilize local binary paerns and Gabor feaures as convenional feaure descripors for face recogniion. This mehod is an unsupervised feaure learning algorihm which is used o learn hierarchical feaure represenaion [6] of he image daabase. Figure 3 Join Feaure Learning block diagram Since differen facial regions have unique physical characerisics, differen dicionaries are used o represen hem. This feaure learning is used o exploi posiion-specific discriminaive informaion for face recogniion. Having learned all hese feaure projecions and represenaions for differen face regions, spaial pooling is performed for face paches o improve he represenaive power of he learned feaures. Moreover, JFL model is sacked ino a deep learning archiecure o sudy he hierarchical informaion for feaure represenaion and face learning. This furher improves he recogniion performance.. Local Binary Paern LBP is a novel approach for face represenaion which considers boh shape and exure daa o represen face images. This ype of recogniion is done using a neares neighbor classifier in he allocaed compued feaure space. LBP allows for very fas feaure exracion and inroduces a discriminaive feaure space which can be applied for boh face deecion and learning challenges. This is based on he concep ha he local differences of he cener pixel and is neighbors are independen of he cenral pixel iself. This heory consiss of dividing an objec image in several regions where he LBP feaures [15] are exraced from he facial image and combined ino a feaure vecor which will be laer used as facial descripor. The main disadvanage of LBP is ha i is sensiive o noise. Local Ternary paern parially solves his problem. Binary Decimal 57 Figure 4 Illusraion of Local Binary Paern Volume 5, Issue 6, June 016 Page 13

4 .3 Local ernary paerns LTP are an exension of Local binary paerns. LTP uses a hreshold consan o divide hreshold pixels ino hree values. Considering he hreshold consan and he value of he cener pixel as k and c and hen he neighboring pixel as p, he resul of he hreshold is, (1) In his above menioned way, each has one of he hree values [10]. Neighboring pixels are concaenaed afer hresholding ino a ernary paern. Calculaing a hisogram wih hese values will resul in a large range and so he ernary paern is being spli ino wo binary paerns [11]. Hisograms are brough ogeher o generae a descripor double he size of LBP. 3. PROPOSED WORK RLTP algorihm is combined wih JFL o increase he accuracy in case of feaure exracion. 3.1 Unsupervised Join Feaure Learning JFL uses K means clusering algorihm which is one of he unsupervised learning algorihm o solve he well-known clusering problem [8]. Robus and discriminaive feaures are exraced o enlarge he iner-personal margins and inra-personal variaions are reduced simulaneously. Usually Local feaure descripors are based on heurisics and compuaion is ime consuming. JFL is used o learn daa adapive feaures direcly from he raw pixel values. Shared informaion among differen regions and posiion specific informaion are simulaneously exploied. 3. Basic Join Feaure Learning and spaial pooling Each facial image is divided ino T non-overlapped regions and feaure represenaion for each region is learn joinly. Then, he learned feaure of he h region is represened as W x, where x is a sample ha consiss of raw pixel values from he divided pach. The values of A and D are analyzed and found. [3]. min H(D, A) D, A γ ( 1 γ T 1 T T 1 ' 1 vec(w T 1 0 α α ) Dα ' F (ma(d α S ' ξ α For each region, hisogram feaure can be exraced by using he corresponding codebook and i is used o concaenae he hisogram feaures from all regions ha are available and urn ino a longer feaure vecor for face represenaion. 3.3 K-means clusering algorihm The main idea is o define k cener and represening one for each cluser. The beer choice is o place he ceners away from each oher. Each poin belonging o a given daa se is aken and associaed o he nearby cener. When here is no poin o be delayed, he firs sep is compleed and an early group age is done. The loop has been generaed in his case. As a resul of his loop, he k ceners are likely o move heir locaion sep by sep unil no more changes are o be done. c c 3.4 Robus Local Ternary paern i J ( V ) ( x i 1 j 1 i Volume 5, Issue 6, June 016 Page 14 v In order o solve he problem of LBP and LTP, Robus LTP is used [5], [4]. Trisae code resuls in a hisogram of many bins and hence i is a mus o reduce is dimensionaliy. Two srong saes which correspond o large pixel differences are o be minimally affeced by image noise and more reliable. The noise easily overcomes he small image difference in Sae X and hence less reliable. Thus Sae X is deduced ino he srong saes. I is difficul o precisely deermine he sign and magniude of small pixel difference. The small pixel difference is equally likely o be posiive and negaive. Thus Sae X is encoded equally ino wo srong saes, i.e. Sae 0 and 1 wih equal probabiliy. As a resul, he rinary code is ransformed back o he binary code. The maximum value of LTP and is complimen is aken for he removal of j ) )) 1 () (3)

5 inra-class variance which has been caused by he LTP due o he discriminaion of brigh objec agains gloom background and dark objec agains a brigh background and his complimen makes he LTP code minimum which is desirable for face recogniion. 3.5 Sacked Join Feaure Learning The feaures exraced are combined for face recogniion which is unique and beer when compared wih he feaure exraced from a single individual layer. Each face image is divided ino several non-overlapping regions and local feaures are learn for each region [13]. Each face image is divided ino one or more non-overlapping regions. Sampling is done in a number of small face image paches in he firs region and flaens hem ino feaure vecors [9] as he inpu o he firs layer of he nework. The weighing marix is mapped o he firs paches. The oupus of his combinaion can be obained a he firs layer and furher oupus are available a he second layer for he oher hree paches, respecively [7]. Having exraced feaures a he firs and second layers, he relaive codebooks are learn and he hisogram feaures are exraced. Finally, he feaures are concaenaed from differen layers and differen regions ogeher as he final feaure represenaion of he whole face image. Figure 5 Join Feaure Learning wih RLTP 4. RESULTS AND DISCUSSION The daase should be seleced so ha i is specific o he propery esed. By selecing any one of he images from he daase, iniial conversion of image ino 4x4 is done. Figure 6 Inpu image is seleced from he daase Training he Join feaure learning on small face paches is done. Figure 7 represens he image from he daase is provided as inpu for he pach formaion. Figure 7 Pach formaion Volume 5, Issue 6, June 016 Page 15

6 Figure 8 Spaial pooling of inpu image Figure 8. Represens he conversion of original image ino gray image. This is daa adapive due o unsupervised clusering. Robus LTP shown in Figure 9 is used for deeper feaure exracion and o eliminae noise. Figure 9 Image processed afer RLTP Concaenaion is done for he hisogram feaures exraced from boh layers ogeher and WPCA is used o map he concaenaed feaure vecor ino a low-dimensional feaure space as he final feaure represenaion of he whole face. Figure 10 and Figure 11 represens he codebook generaion for each face region. Figure 10 Codebook generaion for face region (1:18, 1:18) and (19:56, 1:18) Figure 11 Codebook generaion for face region (19:56, 1:18) and (19:56,19:56) The graph menioned in Figure 1 represens he accuracy level of JFL alone. ROC curve is manipulaed using rue posiive and false posiive values. Volume 5, Issue 6, June 016 Page 16

7 Figure 1 Performance graph of JFL Join Feaure Learning Table 1: JFL mehod - Performance Accuracy ROC range 85.7 True Posiive False Posiive The accuracy range has increased predominanly. The proposed mehod helps in beer feaure exracion. Figure 13 Performance graph of JFL wih RLTP I provides srong resisance o noise compared wih oher supervised algorihms such as LBP and LTP. Hence i is combined wih JFL for beer performance. 5. CONCLUSION Table : Verificaion Performance of exising mehod (JFL wih RLTP) Accuracy ROC range True False Join Posiive Posiive Feaure Learning wih RLTP In his paper, unsupervised feaure learning wih robus local ernary paern approach for face recogniion is proposed. The advanage of his model is ha i provides a feaure learning approach o sudy daa-adapive feaures direcly from available pixel values for face learning and recogniion, so ha higher-order saisics daa can be accuraely characerized. By learning muliple and relaed sparse feaures for differen face regions, more posiion specific discriminaive informaion has been exraced for face represenaion. Volume 5, Issue 6, June 016 Page 17

8 References [1] Ananya Dua, Anikesh, Incenive for using Face Recogniion, he applicaions of his echnology, Inernaional Journal of Science, Technology & Managemen Volume No.03, Issue No. 04, April 014. [] Aruni Singh, Sanjay Kumar Singh, Shrikan Tiwari, Comparison of face Recogniion Algorihms on Dummy Faces, The Inernaional Journal of Mulimedia & Is Applicaions (IJMA) Vol.4, No.4, Augus 01. [3] Déniz.O, Bueno.G, Salido.J, and De la Torre.F, Face recogniion using hisograms of oriened gradiens, Paern Recogni. Le. Vol. 3, no. 1, pp , 011. [4] El Mahdi Barrah, Said Safi, Abdessamad Malaoui, Exended Se of DCT-TPLBP and DCT-FPLBP for Face Recogniion, World Academy of Science, Engineering and Technology Inernaional Journal of Compuer, Elecrical, Auomaion, Conrol and Informaion Engineering Vol: 9, No: 8, 015. [5] Jianfeng Ren, Xudong Jiang and Junsong Yuan, Relaxed Local Ternary Paern for Face recogniion, [6] Jiwen Lu, Venice Erin Liong, Gang Wang, and Pierre Moulin, Join Feaure Learning for Face Recogniion, IEEE ransacions on informaion forensics and securiy, Vol. 10, No. 7, July 015. [7] Kandla Arora, Real Time Applicaion of Face Recogniion Concep, Inernaional Journal of Sof Compuing and Engineering (IJSCE) ISSN: , Volume-, Issue-5, November 01. [8] Lei.Z, Liao.S, Pieikainen.M and Li S.Z, Face recogniion by exploring informaion joinly in space, scale and orienaion, IEEE Trans. Image Process., vol. 0, no. 1, pp , Jan [9] Ma.L and Khorasani.K, Facial Expression RecogniionUsing Consrucive Feedforward NeuralNeworks, IEEEransacions on sysems, ManandCyberneicsParB:Cyberneics, Vol.34,No.3, June 004. [10] Maheswari.C and Vishnu Vardhan.D, DRLBP Based Face-Recogniion, Inernaional Journal of Innovaive Research in Compuer and Communicaion Engineering Vol.3, Special Issue 4, April 015. [11] Richa Sharma, Rohi Arora, Face Recogniion Using LTP Algorihm, Inernaional Journal of Science and Research (IJSR). [1] Riddhi Pael and Shrui B. Yagnik, A Lieraure Survey on Face Recogniion Techniques, Inernaional Journal of Compuer Trends and Technology (IJCTT) volume 5 number 4 Nov 013. [13] Sun.Y, Chen.Y, Wang.X, and Tang.X, Deep learning face represenaion by join idenificaion-verificaion, in Proc. NIPS, 014, pp [14] Sushma Jaiswal, Dr. (Sm.) Saria Singh Bhadauria and Dr. Rakesh Singh Jadon, Comparison beween face recogniion algorihms Eigen faces, Fisherfaces and Elasic Bunch Graph Maching, Journal of Global Research in Compuer Science Volume, No. 7, July 011. [15] Timo Ahonen, Abdenour Hadid and Mai Pieikainen, Face Descripion wih Local Binary Paerns: Applicaion o Face Recogniion, IEEE Transacions on paern analysis and machine inelligence, Vol. 8, No. 1, December 006. Volume 5, Issue 6, June 016 Page 18