Face recognition based on eigenfeatures

Transcription

1 Face recognton based on egenfeatures Paulo Quntlano *a, Antono Santa-Rosa **b and Renato Guadagnn ***c a Federal Polce Department, Computer Scence Coordnaton COINF/DPF, Brasla/DF Brazl b Unversty of Brasla, Computer Vson Laboratory, Brasla/DF Brazl c Catholc Unversty of Brasla, Brasla/DF Brazl ABSRAC Face Recognton s an area of emergent research, that offers great challenges, manly n adverse condtons. hs paper addresses face mages wth approxmately 20% of face n partly occluded or not-well llumnated mages as well as wth use of dsguse, scarf, sun glasses or masks. he presented technques use three dfferent egenfeatures: egeneyes, egennose and egenmouth. Even n these unfavorable stuatons, recognton rates acheved 87%. Images were extracted from "he Yale Face Database. Keywords: Face Recognton, egenface, egenfeatures, egeneyes, egenmouth, egennose. 1. INRODUCION he recognton of well-known faces has a fundamental mportance n our socal relatonshps, comng as a trval functon for our bran, however extremely mportant for our smpler and daly actvtes. Usually, we establsh an nteracton wth people only f face recognton occurs. Face Recognton s a part of a larger context, that s Bometrcs, that gves us the noton of lfe measure. Bometrcs can be defned as the physologc or psychologcal characterstcs that can be used to verfy the ndvdual's dentty. he most used bometrcs are: face, voce, fngerprnt, sgnature, hand geometry, rs and retna [1]. he bometrcs technques are dvded n two categores: physologcal and psychologcal. Physologcal bometrcs are based on physologcal body aspects, and the most common are face, fngerprnt, hand geometry, rs and retna. Psychologcal bometrcs are based on psychologcal aspects, and the most common are sgnature and voce. he bometrcs systems are subjected to the Prncple of hreshold. Accordng to t, a face s recognzed f ts features le nsde on acceptance range. hs prncple defnes some uncertanty degree n results, that mply obtanng more than one answer as a searchng result, eventually needng human nterventon for the correct alternatve choce. Based on ths prncple, the results presented n ths paper consder correct the matches ranked even on 3 rd place. Our work s based on the frst egenface approaches by Krby and Srovch [2] and urk and Pentland [7 and 8]. Recently many approaches tred to perform face recognton n adverse condtons [3 and 5] and to mprove egenfaces [4]. 2. PROPOSED APPROACH he proposed approach allows automated Face Recognton wth fragments of face mages. It works wth half-occluded mages or wth fragments of mages. hus, the egenfaces concepts are expanded to egenmouth, egennose and egeneyes, * quntlano@dpf.gov.br; phone: 55(61) SAIS - Qd. 7 Lt. 23 COINF/DPF Brasla/DF BRAZIL. ** nuno@cc.unb.br; phone: 55(61) Unversty of Brasla Brasla/DF BRAZIL. *** renatov@ucb.br; phone: 55(61) , ext Catholc Unversty of Brasla Brasla/DF BRAZIL.

2 because the algorthm developed based only on egenfaces answers very badly when workng wth half-occluded or ncomplete mages. he technque here presented can be qute useful n polce applcatons, where t s necessary the recognton of people wth several dsguses, coverng part of the face, what usually happens n crmes scenes. By usng the developed technques, automatc face recognton becomes possble wth use of small face parts. Egenmouth and egennose enable automatc face recognton based on the superor part of the covered face or faces wth glasses or masks. he present algorthms are very smlar to the egenface algorthms. However, they are supposed to have an "addtonal ntellgence" to verfy the part of the face that should be automatcally submtted to recognton. We also have to mantan a much more complete database, wth specfc nformaton of egenfaces, egenmouth, egennose and egeneyes, about all worked classes (each class s a dfferent people), always wth the same crteron establshed n the extracton and matches of all these egenfeatures used. 3. FACE DAABASE We used the wthglasses, happy, noglasses, normal, sad, sleepy, surprsed and wnk mages from "he Yale Face Database", suppled by Yale Unversty. Fgure 1 shows the 15 classes of the database n dfferent facal expressons and llumnaton condtons. hs database, whose mages have 243x320 pxels, offer several good challenges to any Face Recognton project. For evaluaton and tests, we extracted 64x64 pxels fragments from the faces mages, n three stuatons: (1) wth the an eye, (2) wth the nose and (3) wth the mouth. Fgure 1: 15 people wth dfferent facal expressons and llumnaton condtons. 4. MEHODOLOGY We used a set of M=120 face mages, dentfed as Γ ( = 1,..., M ), for verfcaton and testng. he extracted fragments of mages are NxN square matrces, wth N=64. At frst, all those M mages are transformed nto a column vector, wth the 2 2 N x1 dmenson, wth the same N pxels. hs converson s performed takng each one lnes and concatenatng them, one after other, buldng the column vector, n the followng way: ' 2 Γ = Γ ( = 1,..., N ; j, k 1,..., ) (1), 1 j, k = N hen, we calculate the average features of all mage set, addng all the mages and dvdng the result by the amount of mages, n the followng way:

3 Ψ = M 1 (2) Γ M = 1 Once calculated the average features Ψ, we set up a new group of mages Φ, obtaned from the dfference between each mage of the tranng set and the average features. Fgure 2 shows some face mages features. Fgure 2: A to C are the Average Mouth, Average Eye and Average Nose, D to F are Average Eyes from ts classes, and G and H are exemples of mages from the tranng set. hus, each mage Φ dfferentates from the average features of the dstrbuton, and ths dstance s calculated subtractng them from average feature, arrvng to a new space of mages, calculated n the followng way: Φ = Γ Ψ( 1,..., M ) (3) = From the new set of the M mages, we set up the matrx A, of dmenson ( N 2 xm ), takng each M vectors of Φ and placng them n each column of the matrx A, n the followng way: A = Φ (4), j j;,1 From the matrx A, we tred to set up the covarance matrx C, wth dmenson N 2 xn 2, n the followng way: C = AA (5) As the dmenson of that matrx s very bg, the covarance matrx L can be set up, wth dmenson MxM, wthout the assembly of matrx C. he calculaton of matrx L s performed n the followng way: L = A A (6) he egenvectors of C are calculated from the egenvectors of L, obtanng them from lnear combnaton of the orgnal mages space wth the egenvectors of L (matrx V), n the followng way: U = AV (7) Where matrx V, of dmenson MxM s consttuted by the M egenvectors of L and matrx U, of dmenson ( N 2 xm ) s consttuted by all the egenvectors of C, and the matrx A s the space of mages, of dmenson ( N 2 xm ).

4 4.1 ranng the model After egenfeatures are extracted from the covarance matrx of a faces set, tranng stage takes place. For that, we used just 2 mages of each class, and we generated three Average Features (mouth, eye and nose) for each class from those mages (see Fgure 2), so we stored a database wth nformatons about egenfaces and those three egenfeatures n order to perform face recognton from any egenfeature. And for the verfcaton and tests we used all the M mages of the tranng set, usng egenface and all egenfeatures used. We know that just some few egenvectors wth the larger egenvalues s necessary for the face recognton, so we just used M <M egenvectors (See tables 1 to 4). hs projecton s performed n the followng way: Ω = U ( Γ Ψ), 1,..., Nc. (8) = he matrx Ω, wth dmenson ( M xnc), contans the Nc egenvectors, wth dmenson ( M x1), of matrx L, and t s used for comparson wth the new faces presented for comparson effect and recognton. Nc s the number of exstent classes n the tranng set. 4.2 Face recognton from egenfeatures he egenfeatures are used to represent or to code any face that we tred to compare or to recognze. Fgure 3 shows mages reconstructed from the egenfeatures wth hgh egenvalues. hen we should use egenfeatures wth hgher egenvalues n the reconstructon, because they provde much more nformaton about the varaton of faces. o perform Face Recognton, the algorthm frstly choose the best egenfeature (egeneyes, egennose or egenmouth), dependng on the mage condton, the most favourable egenfeature s chosen. hus, the descrptors of the new face egenfeature are extracted and compared wth the descrptors of the classes stored n the database, calculated n the same way, usng the Eucldean dstance. So, each face submtted to Face Recognton s projected n the Feature Space, obtanng the vector Ω, n the followng way: Ω = U ( Γ Ψ) (9) he vector Ω, of dmenson (Mx1), s compared wth each vector Ω ( = 1,..., Nc). If the dstance found among Ω and any Ω ( = 1,..., Nc) s nsde the threshold of the class and t s the smallest found dstance, then t has had the face recognton of Ω belongng to the class. We calculated ths dstance by the square mnm method, n the followng way: 2, 2 ε = Ω Ω ( = 1,..., Nc) (10) Fgure 3. Some egenmouth of the Average Mouth, from egenvectors wth larger egenvalues.

5 4.3 Calculatng thresholds We found out up to 10 thresholds for each worked classes, lookng for a better performance on face recognton. he thresholds θ ( = 1,..., Nc) defne the maxmum dstance allowed among the new face submtted to recognton and each classes. If the dstance found between the new face and one of the classes s nsde the threshold of the class, then t has had the face recognton. he Nc thresholds s calculated n the followng way: 1 θ = max{ Ω Ω j } (, j = 1,..., Nc) k On ths approach we use factor k from 1 to 10. If ths factor s lttle (near to 1), we have a bg false-postve rate and a lttle false-negatve rate. Otherwse, f ths factor s bg (near to 10), we have a lttle false-postve rate and a bg false-negatve rate. (11) 5. RESULS All presented results were obtaned usng the same M=120 mages, wth one processng for each amount of presented egenvectors. able 1 presents the results obtaned wth the egenfaces algorthm. able 2 presents the results obtaned wth the algorthms based on egenmouth. able 3 presents the results obtaned wth algorthms based on egeneyes. able 4 presents the results obtaned wth algorthms based on egennose. able 1. Results obtaned wth the egenfaces algorthm. ERRORS SUCCESS UNIL 3 RD VECORS (M') % % % % % % % % able 2. Results obtaned wth the egenmouth algorthms. ERRORS SUCCESS UNIL 3 RD % % % % % % % % able 3. Results obtaned wth the egeneyes algorthms. ERRORS SUCCESS UNIL 3 RD VECORS (M') % % % % % % % % able 4. Results obtaned wth the egennose algorthms. ERRORS SUCCESS UNIL 3 RD VECORS (M') % % % % % % % %

6 About the processng of the egenfeatures algorthms, we can observe, as vsually, from Fgure 3, as based on the tables wth the obtaned results, that these algorthms have dfferentated behavor comparng them wth egenfaces algorthm. he performance of the egenfaces s well behaved, mprovng lnearly as the number of egenvectors used ncreases, whle the actng of the egenfeatures algorthms are a lttle unexpected, oscllatng lghtly up and down when always submtted to a growng varaton of amount of used egenvectors. hs can be observed on the ables 2, 3 and 4. Based on the Prncple of hreshold, the recognton s acceptable when the Eucldean dstance found s ranked up to the 3 rd place and s nsde the defned threshold. hs prncple s qute acceptable because of the great complexty of face representaton and the proxmty of the found results, untl the 3 rd place and nsde the defned threshold. 6. CONCLUSION We verfed egenface approach s qute robust n treatment of face mages wth vared facal expressons and transparent glasses. However, t s very senstve n the treatment of face mages wth dsguse, scarf, sun glasses and masks. Our approach can perform face recognton under these complcated condtons, wth recognton rates over 85%. In spte of egenfeatures algorthm uses only about 20% of face mages ts performance s only a lttle worse than egenfaces algorthm, that uses whole face mages, as shown on ables above. Egenfeatures algorthm has the same advantages of egenfaces algorthm, t s qute effcent and smple n the tranng and recognton stages too, dspensng low level processng to verfy the face geometry or the dstances between the facal organs and ts dmensons. REFERENCES 1. Ashbourn, Julan, he ruth about Bometrcs, he Journal of the Assocaton for Bometrcs, Krby, M. and Srovch, L., "Applcaton of the Karhunen-Loeve Procedure for the Charactersaton of Human Faces", IEEE ransactons on Pattern Analyss and Machne Intellgence, Klasén, Lena and L, Habo, "Faceless Identfcaton", Swedsh Natonal Laboratory of Forensc Scence, Sweden, Face Recognton From heory to Applcatons, NAO ASI Seres, Seres F: Computer and Systems Scences, Vol. 163, Sprnger-Verlag Berln Hedelbert, Moghaddam, Baback; Wahd, Wasuddn and Pentland, Alex, "Beyond Egenfaces: Probablstc Matchng for Face Recognton", MI Meda Laboratory Perceptual Computng Secton echncal Report No. 443, Appears n: he 3 rd IEEE Int'l Conference on Automatc Face & Gesture Recognton, Nara, Japan, Aprl Quntlano, Paulo; Guadagnn, Renato and Santa-Rosa, Antono, "Practcal Procedures to Improve Face Recognton Based on Egenfaces and Prncpal Component Analyss", Pattern Recognton and Image Analyss, Moscow, v. 11, n. 2, pp , Sobottka, Karn and Ptas, Ioanns, Localzaton of Facal Regons and Features. he 4-th Open Russan-Geerman Workshop Pattern Recognton and Image Analyss, Novgorod State Unversty, he Russan Federaton, March 3-9, urk, Matthew e Pentland, Alex, Egenfaces for Recognton, Vson and Modelng Group, he Meda Laboratory, MI, In Journal of Cogntve Neuroscence, Volume 3, Number 1, pages 71-86, urk, Matthew e Pentland, Alex, "Face Recognton Usng Egenfaces", Proceedngs of the IEEE Computer Socety Conference on Computer Vson and Pattern Recognton, June Mau, Hawa, pages , 1991.