Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 2016

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1 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 doi: / Face Deecion and Recogniion Based on an Improved Adaboos Algorihm and Neural Nework Haoian Zhang*, Jiajia Xing, Muian Zhu, Dan Wu, Zheyu Yang Deparmen of Engineering and Compuing Sciences, New York Insiue of Technology, 1855 Broadway, New York, NY , USA School of Overseas Educaion, Nanjing Universiy of Poss and Telecommunicaions, Nanjing, Jiangsu, 21116, China *Corresponding auhor Absrac In recen years, face deecion and face recogniion based on images have been widely used in various fields. This paper focuses on face deecion mehods based on he Adaboos algorihm, opimizes and improves he weigh updaing rules of he Adaboos face deecion algorihm. Resuls indicae ha he opimized Adaboos algorihm has no only been grealy improved in compuing speed and face posiioning effec, bu also been inegraed ino a neural nework model for face idenificaion. I is found ha no only faces of poor image qualiy can be recognized, he compuing speed and ani-inerference abiliy are beer han radiional face deecion and recogniion algorihms. Therein, he comprehensive face recogniion rae in ORL daabase reaches more han 98%. The recogniion rae is high. 1. INTRODUCTION Face deecion is a key echnology in face informaion processing. Reliable face deecion is a premise of efficien face recogniion(turk M and Penland A, 1991; Sirovich L and Kirby M, 1987).Among hem, he use of face informaion mainly includes wo aspecs: face deecion and face recogniion. Face deecion is he firs link in he exracion of face informaion, whose deecion performance will direcly affec he exracion of face informaion and furher affec subsequen face recogniion. In recen years, face deecion and face recogniion echnology based on saic images and dynamic videos has been brough o grea aenion in he field of paern recogniion and compuer vision (Reed T R and Du Buf J M H, 1993; Chen C H and Pan L F, 1993;Brooks R A, 1983; Pope A R, 1994; Chin R T and Dyer C R, 1986). Generally speaking, curren face deecion mehods can be divided ino wo ypes: deecion based on knowledge and deecion based on saisics (Brunelli R and Poggio T,1993). Face deecion based on knowledge mainly regards face as a combinaion of organ feaures according o prior knowledge. I deecs faces based on feaures of eyes, e yebrows, mouh, nose and oher organs, as well as heir relaive geomeric posiions. Face deecion based on saisics, however, regards face as a 2D daa marix and judge wheher a face exiss or no from similariy, by consrucing face paern space. This paper carries ou an in-deph sudy ino face deecion mehods based on Adaboos, pus forward an improved he opimized Adaboos algorihm, exracs all face informaion in an image using his model and inegraes i ino a neural nework model(brosch T and Tam R, 215), o deec and recognize various faceconaining images (Y. Boureau and F. Bach, 21; I. J. Goodfellow and Q. V. Le, 29; G. E. Hinon, S, 26; J. Yang and K. Yu, 29; Marina E. Plissii and HrisophorosNikou, 211; Q. C. Chen and Q. S. Sun, 28). Experimenal resuls show ha boh he face deecion and face recogniion raes of he opimized Adaboos algorihm inegraed ino neural nework model in his paper are high. No only images of poor qualiy can be processed, he compuing speed and ani-inerference abiliy are beer han radiional face deecion and recogniion mehods. 2. OPTIMIZATION OF THE ADABOOST FACE DETECTION ALGORITHM The core idea of radiional Adaboos algorihms is aimed a he same raining daabase. Several differen weak classifiers are produced in he process of raining and some beer classifiers are combined ino ulimae srong classifiers. The raining process only includes: (1)To exrac Haar feaures in an image; (2)To ransform he Haar feaures ino corresponding weak classifiers; (3)To selec he bes weak classifiers from a large number of weak classifiers and combine hem ino opimal srong classifiers. However, he daa size during face deecion and face raining is large. Generally each weak classifier has ens of housands of raining samples. When here are many sample objecs ha are difficul o disinguish in raining samples, ofen as rainings imes increase, he weighs of indisinguishable sample images will be high and model raining will focus on his par of samples. As a resul, he raining will be oo long and over fiing will occur in model resuls. Therefore, his paper improves he weigh updaing algorihm in he Adaboos

2 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 raining process and judges wheher a weigh needs o be updaed by seing a weigh updaing hreshold. The deailed calculaion seps of he opimized model are as follows: Sep 1: To classify and number N raining samples, where m face samples are labeled as y i =1 and N-m non-face samples are labeled as y i =-1. Sep 2: To iniialize he weighs of N raining samples. Sep 3: In he process of he h ieraion raining, selec T weak classifiers, and calculae he ieraion error sum of T weak classifiers see formula (1) and relaive error raio see formula (2). N w h y (1) i i1 ij i i (2) 1 Sep 4: In each ieraion, calculae he hreshold HW. When various image weighs are updaed, if a weigh is greaer han HW or a sample belongs o a correc class, he weigh will no longer be adjused. Oherwise, when a sample is wrongly classified and he curren weigh D i is smaller han he hreshold HW of curren ieraion, he weigh of image should be adjused. The calculaion formulas of weigh adjusmen expression and hreshold HW in each ieraion are as follows: a e hi yi Di a Di 1 e hi yi, Di HW (3) Zi a e hi yi, Di HW Di i (4) HW 1 N Where Z is he normalizaion facor of Di 1( xi ), generally D i 1 ( x 1 ). HW is he weigh updaing hreshold of he h ieraion. Sep 5: Afer a model raining ieraion is compleed, several beer classifiers will be combined linearly o ge srong classifiers. As shown below, hey will be conneced in series o form a sric cascade classifier. T 1 T 1 (log1 ) ( ) log1 1 h x 1 h ( ) 2 f x (5) T 1 T (log1 ) ( ) log1 1 h x FACE DETECTION EXPERIMENT ON THE OPTIMIZED ADABOOST ALGORITHM The above improved Adaboos algorihm is used for face deecion. Images of differen qualiies are esed respecively. Experimenal resuls show ha he opimized Adaboos algorihm has beer face deecion performance. The resuls are shown in Table 1 below, and par of he face deecion resuls are shown in Fig. (1) below: Table 1. Face Deecion Resuls Real Faces Deeced Faces Deecion Rae% Loss Rae% Error Rae% Image Qualiy N

3 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 Figure 1. Par of he Face Deecion Resuls From he above experimenal resuls of he opimized Adaboos face deecion model, we can see ha for images of differen qualiies, face deecion can sill be achieved perfecly. Boh he compuing speed and aniinerference abiliy are beer han radiional Adaboos face deecion algorihms. 4. FACE RECOGNITION EXPERIMENT ON THE OPTIMIZED ADABOOST INTEGRATED INTO NEURAL NETWORK MODEL The purpose of face recogniion is o exrac personalized feaures from face images, so as o idenify he ideniy of a person. A complee face recogniion sysem mainly includes face deecion, face sandardizaion, face characerizaion and face recogniion. From he above face deecion experimen, we can see ha for a given image, he opimized Adaboos algorihm can basically deec all exising faces in an image. In order o fully demonsrae ha he opimized Adaboos model can provide favorable values for subsequen face idenificaion, on his basis, his paper probes ino a face recogniion model based on he opimized Adaboos algorihm inegraed ino neural nework. The face recogniion model flows of he opimized Adaboos algorihm inegraed ino neural nework designed in his paper are as follows: Sep 1: Firs of all, unify pixel sizes of all face raining samples as 2*5. Exrac each line of pixels according o pixel levels and ransform hem ino a column marix line by line as inpu informaion o rain BP neural nework model. The deailed nework convergence and nework srucure parameers in he firs deecion and recogniion are shown in Table 2 below: Sep 2: To use he opimized Adaboos algorihm o deec faces in a given image and exrac all of hem. Sep 3: To process pixels in he exraced face areas in Sep 2, according o Sep 1. Sep 4: To inpu face informaion derived from Sep 3 ino a neural nework model and recognize relevan faces. Par of he face deecion and recogniion resuls of he opimized Adaboos algorihm inegraed ino neural nework model is shown in Fig. (2) below:

4 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 Figure 2. Resuls of BP Nework Model Training Convergence Table 2. Key Parameers in Nework Training Seing and Nework Convergence Nework Training Parameers Nodes a he firs level Nodes a he second level Nework arge error Nework raining funcion *e-8 rainrp Nework Convergence Parameers Nework raining imes Nework raining ime (sec) Nework convergence error Nework goodness of fi *1-8 1 (a) (b) Figure 3. Face Recogniion Resuls of he Opimized Adaboos Algorihm Inegraed ino Neural Nework Model. Fig. (a) The deecion and recogniion of differen expressions of he same face; Fig. (b) The deecion and recogniion of differen faces and expressions

5 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 The above opimized Adaboos model inegraed ino neural nework is used for face recogniion. The face daa base involved is ORL face daa base from Olivee Lab. There are a oal of 4 face images in ORL daabase. This daabase includes 4 persons and 1 face images of each. The resoluion of each image is 92 pixels by 112 pixels. In order o ensure he generaliy of experimen, 4 images of each person are seleced and made ino raining samples during experimen. The remaining face images of each person (6) are made ino es samples. During es, he raining samples and es samples are esed separaely for 5 imes. The derived 5 comprehensive recogniion raes are averaged, as recogniion accuracy rae of he final model. The recogniion resuls are shown in Table 3 below: Tes Times Table 3. Face Recogniion Rae of he Opimized Adaboos Model Inegraed ino Neural Nework Training Samples Tes Samples Recogniion Accuracy of Training Samples Recogniion Accuracy of Tes Samples Comprehensive Recogniion Accuracy CONCLUSION By opimizing and improving he weigh updaing rules of he Adaboos face deecion algorihm, his paper prevens model raining from focusing on difficul samples a he laer sage and oo large weighs of difficul samples, caused by he exisence of difficul raining samples. By seing new weigh updaing rules, overmaching is avoided. Resuls show ha he opimized Adaboos algorihm has no only been grealy improved in compuing speed and face posiioning effec, bu also been inegraed ino a neural nework model for face idenificaion. I is found ha no only faces of poor image qualiy can be recognized, he compuing speed and ani-inerference abiliy are beer han radiional face deecion and recogniion algorihms. Therein, he comprehensive face recogniion rae in ORL daabase reaches more han 99%. The recogniion rae is high. CONFLICT OF INTEREST The auhors confirm ha his aricle conen has no conflics of ineres. Acknowledgmen This work is suppored by he Key Projec of Science and Technology Innovaion Training Program (STITP) No. XZD REFERENCES Turk M, Penland A. (1987) Eigenfaxes for recogniion,.journal of Cogniive Neuroscience,3(1), pp Sirovich L, Kirby M. (1987) -dimensional procedure for he characerizaion of human faces.journal of he Opical Sociey of America A:Opics Image Seience and Vision,4(3), pp Reed T R, Du Buf J M H. (1993) A review of recen exure segmenaion, feaure exracion echniques.cvgip Image Undersanding,1993(57), pp Chen C H, Pan L F, Wang P S P.(1993) Handbook of Paern Recogniion and Compuer Vision. Singapore: world Scienific Publishing, pp Brooks R A. Model-based hree-dimensional inerpreaions of wo-dimensional images.paern Analysis and Machine Inelligence, 1983,5(2), pp Pope A R. (214) Model-Based Objec Recogniion: A Survey of Recen Research. Technical Repor, 1994, 22(11), pp Chin R T, Dyer C R. (1986) Model-Based Recogniion in Robo Vision. Compuing Surveys,18(1), pp Brunelli R, Poggio T. Face recogniion: Feaures versus emplaes. Paern Analysis and Machine Inelligence, 1993,15(1), pp Brosch T, Tam R.(215) Efficien Training of Convoluional Deep Belief Neworks in he Frequency Domain for Applicaion o High-Resoluion 2D and 3D Images. Neural Compuaion, 27(1), pp Y. Boureau, F. Bach, Y. LeCun, and J. Ponce. (21) Learning mid-level feaures for recogniion. In IEEE Conference on Compuer Vision and Paern Recogniion. Pp

6 Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, , 216 I. J. Goodfellow, Q. V. Le, A. M. Saxe, H. Lee, and A. Y. Ng. Measuring invariances in deep neworks. In NIPS, 29. G. E. Hinon, S. Osindero, and Y. W. Teh. A fas learning algorihm for deep belief nes. Neural Compuaion. 26, 18(7), pp J. Yang, K. Yu, Y. Gong, and T. S. Huang. Linear spaial pyramid maching using sparse coding for image classificaion. In CVPR, 29, Marina E. Plissii, HrisophorosNikou. Anonia Charchani, Combining shape, exure and inensiy feaures for cell nuclei exracion in Pap smear images, Paern Recogniion Leers. 211, 23, pp Q. C. Chen, Q. S. Sun, P. A. Heng and D. S. Xia, "Adouble-hreshold image binarizaion mehod based on edge deecor", Paern Recogniion. 28, (41), pp