Sxth Indan Conference on Computer Vson, Graphcs & Image Processng Incorporatng Cohort Informaton for Relable Palmprnt Authentcaton Ajay Kumar Bometrcs Research Laboratory, Department of Electrcal Engneerng Indan Insttute of Technology Delh, Hauz Khas, New Delh 110016, Inda Emal: ajaykr@eee.org Abstract Ths paper presents a new approach to acheve the performance mprovement for the tradtonal palmprnt authentcaton approaches. The cohort nformaton s used n the matchng stage but only when the matchng scores are nadequate to generate relable decsons. The cohort nformaton can also be utlzed to acheve the sgnfcant performance mprovement for the combnaton of modaltes and ths s demonstrated from the expermental results n ths paper. The rgorous palmprnt authentcaton results presented n ths paper are the best n the lterature and confrm the utlty of sgnfcant nformaton that can be extracted from the mposter scores. The statstcal estmaton of confdence level for the palmprnt matchng requres an excellent match between the theoretcal dstrbuton and the real score dstrbuton. The performance analyss presented n ths paper, from over 29.96 mllon mposter matchng scores, suggests that Beta-Bnomal functon can more accurately model the dstrbuton of real palmprnt matchng scores. 1. Introducton The hand-based bometrc authentcaton has hgher useracceptance and recevng ncreasng nterest n recent years. The palmprnt mages have larger area and thus more abundant features whch are qute unque even among dentcal twns. The usage of these systems for large scale personal authentcaton requres further efforts to acheve sgnfcant performance mprovement. In ths work my efforts are focused to acheve further performance mprovement on the promsng approaches presented n the lterature. Daugman [8] has presented an optmal method for rs representaton based on the dstrbuton of a Hammng dstances from the rscode. Ths dstrbuton s estmated from more than 9 mllon dfferent rs comparsons and shows a Bnomal trend whch peaks at a normalzed hammng dstance of 0.5. Despte the popularty of palmprnt bometrc, there has not been any effort to establsh the theoretcal dstrbuton of real palmprnt matchng scores to ascertan the confdence n decsons. 1.1 Pror Work Several approaches for palmprnt authentcaton usng lne features, appearance-based features, and texture-based features have been presented n the lterature [1]-[6]. Recent comparatve study of palmprnt authentcaton approaches n [5] has suggested that the ordnal representaton delvers best performance on peg-free palmprnt database. The authors n [5] have also compared the performance of the ordnal features wth those based on Gabor phase encodng presented earler n [4], [6] and llustrated promsng results on PolyU palmprnt database [12]. The hand geometry features can be smultaneously extracted from the hand mages and utlzed to enhance the palmprnt dentfcaton performance [10]. The usage of such approach for hand dentfcaton usng contactless magng has been llustrated n [3]. Jan and Demrkus [2] have recently presented a new approach for automated palmprnt matchng usng latent palmprnts. Authors have suggested to utlzed the frcton rdges, flexon creases and mnutae ponts that can be smultaneously extracted from 500 dp palmprnt mages. 1.2 Proposed Approach In ths paper a new approach s nvestgated to mprove the performance of tradtonal palmprnt dentfcaton systems that have been presented n the lterature. The performance mprovement s nvestgated by ntegratng cohort nformaton n the decson makng. The expermental results are presented on the publcly avalable palmprnt database [12] from 386 dfferent palms and also from the publcly avalable touchless palmprnt database [15] from 235 subjects. Ths work also detals the performance mprovement results usng the cohort nformaton for the score level combnaton of bmodal dataset from whch hand geometry and palmprnt features are smultaneously extracted. The expermental results suggest sgnfcant mprovement n the performance as compared to the prevously presented approaches n the lterature [4]-[6]. Another aspect of ths work s focused on the accurate estmaton of theoretcal model for the palmprnt matchng scores. The rgorous expermental results from over 29.96 mllon comparsons on the publcly avalable database suggest that the Beta- Bnomal dstrbuton s the most approprate model for the palmprnt score characterzaton. 2. Integratng Cohort Informaton The block dagram for the palmprnt authentcaton system usng cohort nformaton s shown n fgure 1. The acqured palmprnt mages from the every user are used to extract the (ordnal) features and matched wth the correspondng user template (ordnal features). If ths matchng score S m s less than the decson threshold (T) than the user s authentcated as genune. However, we do 978-0-7695-3476-3/08 $25.00 2008 IEEE DOI 10.1109/ICVGIP.2008.73 583
not reject ths user f hs score S m s more than threshold but employ cohort nformaton to ascertan f he/she s genune or mposter. Ths requres addtonal computatons of matchng scores S ( = N 1) for the tranng data from all the cohort templates. The matchng 1 2 scores S between the two palmprnt samples f and f from each of the = 1,, N users s defned as; 1 2 S = Θ( f, f j ) for j (1) where Θ denotes the matchng scores between sample f and f. If the score S m s less than all the cohort 1 2 matchng scores S, then the user s authentcated as genune user. However, even f any of the cohort scores,.e. S, s less than S m then the user s authentcated as mposter user. Fgure 1: Palmprnt authentcaton usng cohort nformaton. 3. Palmprnt Score Dstrbuton Model The performance evaluaton of bometrc system requres accurate methodologes for assessng the dstrbuton of real matchng scores. The beta-bnomal dstrbuton s a generalzaton of bnomal dstrbuton and a more approprate model for performance evaluaton snce t also ncorporates the ntra-user correlaton between n matchng attempts. The preference of Beta-Bnomal dstrbuton, over bnomal dstrbuton, has been llustrated n several publcatons [11], [13]-[14] and cted as most approprate. However, the bnary nature of outcomes from the matchng decsons suggest that the Bnomal dstrbuton can be more approprate for analyzng the bometrc matchng scores [8]. In ths work, the accuracy of best ft model s ascertaned from the emprcal estmaton of the mean square error from the best ft generated from () Beta () Bnomal, () Beta- Bnomal and (v) Gaussan dstrbuton. Let us assume that the probablty assocated wth each of th s ndvdual = 1, 2, m s p. Each of these p come from the condtonally ndependent draws that are characterzed by beta dstrbuton. The Beta dstrbuton s characterzed by two parameters (α and β) and ts probablty dstrbuton s as follows. Γ( α + β ) α 1 β 1 f ( p α, β ) = p (1 p ) (2) Γ( α) Γ( β ) The mean and varance for the above beta functon B(α, β) s as follows: E p α, β ] = α /( α + β ) = π (3) [ Var [ p α, β ] = π (1 π ) /( α + β + 1) (4) The Bnomal dstrbuton Bn(n, p ) for the m ndvduals, wth each of the ndvduals tested n tmes wth X number of successes, has the followng probablty functonal form: n x n x f ( x n ) = p p x (1 ) (5) The uncondtonal dstrbuton of X havng Beta-Bnomal dstrbuton has the followng probablty functon: n B( α + x, β + n x ) f ( x n, α, β ) = x (6) B( α, β ) If we assume that the X are condtonally ndependent, then the mean and varance of beta-bnomal dstrbuton Betabn α, β, n ) can be estmated as follows: ( E [ X ] = n π, Var [ X ] = n π (1 π ) η (7) where η = ( α + β + n ) /( α + β + 1) The detaled dervaton of (7) and varous fundamental propertes of beta-bnomal dstrbuton have been descrbed n [11], [13]. 4. Experments and Results The performance mprovement usng cohort nformaton for the palmprnt bometrc s regrously nvestgated n two separate set of experments. Frstly, the peg-free hand mage database that can also smultaneously extract the hand-geometry features s employed. The extracton of palmprnt and hand-geometry mages, feature extracton and the matchng crtera employed s same as detaled n [3]. Each of the 300 300 pxels palmprnt mage s dvded nto 24 24 pxels overlappng blocks. The extent of ths overlappng has been emprcally selected as 6 pxels. Thus we obtan 144 separate blocks from each palmprnt mage. The standard devaton of dscrete cosne transform (DCT) coeffcents, obtaned from each of the overlappng blocks, s used to characterze the palmprnt regon. The 17 features that can characterze every hand-shape mages;.e. permeter, 4 fnger length, 8 fnger wdth, palm wdth, palm length, hand area, and hand length, are extracted. The frst fve mages were 584
Fgure 2: Acqured hand mage (left), extracted palmprnt (rght-top) and correspondng hand geometry mage (rghtbottom) employed for the tranng and rest mages were employed for the testng. As detaled n secton 2 (fgure 1), the cohort nformaton, from each of the mposter matches, was ntegrated n the decson makng separately for the palmprnt and hand geometry authentcaton. The recever operatng characterstcs (ROC) from the palmprnt matchng for two cases,.e. wthout and wth the usage of cohort nformaton, s shown n fgure 3. Smlarly, the ROC for the hand geometry authentcaton, wth and wthout usage of the cohort nformaton s shown n fgure 4 (upper). In addton, the utlty of the cohort nformaton for the performance mprovement usng cohort nformaton was also ascertaned. The palmprnt and hand geometry matchng scores were combned usng hyperbolc product combnaton to ascertan the performance from the combnaton of two modaltes. The hyperbolc product combnaton generates the combned score from tanh( s p * sh), where the s p s the normalzed palmprnt scores and s hg s the normalzed hand geometry scores. Ths combnaton was emprcally evaluated aganst the sum, product, weghted sum rule Fgure 3: The performance from the palmprnt features wth and wthout the usage of cohort nformaton. Fgure 4: The performance from the hand geometry features (top), score level combnaton of palmprnt and hand geometry features (bottom) usng cohort nformaton. and found to generate better performance. The ROC from ths score level combnaton, wth and wthout usage of the cohort nformaton s also shown n fgure 4. The equal error rate from ths frst set of experments s summarzed n table 1. These expermental results consstently suggest sgnfcant mprovement n the performance wth the usage of cohort nformaton. The second set of experments was exclusvely focused to ascertan the performance mprovement from the publcly avalable PolyU palmprnt database [12] usng three approaches. Ths database contans palmprnt samples from the 386 palms (193 ndvduals) and the number of mages employed from each of the users were kept unform (18 samples) n these set of experments. 585
Fgure 5: Image sample (left) from palmprnt database n [12] and the extracted correspondng palmprnt mage (rght) The method of automatcally extractng the regon of nterest from the avalable mages was same as detaled and employed n [4]. The bnarzed feature extracton, from the phase-encodng of regon of nterest, usng the OrdnalCode [5] PalmCode [4], and the CompCode [6] was nvestgated. The parameters of the Gabor flter and the method for extractng features was the same as detaled n [4] and [6]. However, the parameters for the Ordnal feature extracton were emprcally selected (flter sze 35 35, δ x = 3, δ y = 10) as the correspondng parameters avalable n [5] are selected of smaller verson of ths database (verson 1.0). The Hammng dstances from these three approaches, usng the parameters as detaled n the correspondng references, for all the possble genune and mposter matches were generated whle ensurng that no genune and mposter par matches are repeated [8]. Thus we employed 59058 (153 386) gennunes and 22737330 (18 385 386) mposters to nvestgate the performance mprovement. The ROC from the method n [5], [6] and [4], along wth the usage of cohort nformaton, s llustrated n fgure 6, 7, and 8 respectvely. The equal error rate from each of these three cases s summarzed n table 2. These expermental results are consstent and suggest that sgnfcant performance mprovement can be acheved wth the usage of cohort nformaton. Fgure 7: The performance from the CompCode features wth and wthout the usage of cohort nformaton. Fgure 8: The performance from the PalmCode features wth and wthout the usage of cohort nformaton. Fgure 6: The performance from the OrdnalCode features wth and wthout the usage of cohort nformaton. Another set of experments were focused to nvestgate the performance mprovement from peg-free and touchless palmprnt mage database acqured at IIT Delh. Ths database has been acqured from 235 subjects usng smple magng setup detaled n [15]. The mages n IITD palmprnt database have hgh pose, translaton, and scale varatons resultng from unconstraned and touchless magng. The nter-fnger valleys (frst and thrd) ponts are used as reference ponts for the extracton of palmprnt regon from the acqured mages. The varyng sze palmprnt regon/mages are automatcally normalzed to fxed sze of 150 150 pxels. Fgure 9 shows sample mages from ths database and the correspondng segmented mages after the normalzaton. 586
Fgure 9: Acqured mage samples from two subjects n touchless IITD palmprnt database and the correspondng segmented mages of 150 150 pxels after normalzaton. Fgure 10: The performance from the CompCode features wth and wthout the usage of cohort nformaton. Fgure 11: The performance from the PalmCode features wth and wthout the usage of cohort nformaton. Therefore varyng detals of palmprnt texture are observed from the normalzed mages of same subject, unlke PolyU palmprnt database whch has neglgble or It can be observed from ths set of sample mages that the touchless magng results n uneven mage scale changes and the varaton of pxel densty n the palm regon. 587
(a) (b) (c) (d) (e) Fgure 12: Estmaton of matchng score dstrbuton for mposter and genune matches from Ordnal representaton n (a) and (b), from PalmCode n (c) and (d), from CompCode n (e) and (f) respectvely. (f) 588
(a) Fgure 13: Estmaton of matchng score dstrbuton for mposter and genune matches from the DCT representaton n (a) and (b) respectvely. (b) very lttle scale varatons. In ths work, four mages were employed for the tranng and the remanng mage was used for testng. The average of test results, when each of the fve mages are used for testng, are reported n ths work. The Gabor flters of sze 65 65, centered at frequency 2 2, were employed to extract CompCode and PalmCode features. The performance from these two set of features s shown n shown n fgure 10 and fgures 11. It can be observed from ths fgure that the usage of cohort nformaton sgnfcantly mproves the performance, especally at lower FAR, whch s most lkely to be the operatng pont of the system usage. The expermental results from the OrdnalCode also llustrate sgnfcant performance mprovement but are not reproduced due to space constrants. The next set of experments were focused to ascertan the best statstcal model for the real dstrbuton of genune and mposter scores obtaned from the palmprnt bometrc. In ths set, all the avalable mages from every subject, from the PolyU database [12], were employed to generate the maxmum possble number of genune and mposter matches. Thus all 74068 genune and 29968808 mposter matchng scores were generated fromall the 7752 palmprnt mages usng the PalmCode, OrdnalCode and CompCode representatons and were employed to generate the best ft plot from Beta, Bnomal, Beta-Bnomal and Gaussan models. The mean square error from the best set of parameters was computed to ascertan the best match. Thus the parameters for the each of these four models were dfferent and emprcally selected such that the mean square error between resultng dstrbuton and the real palmprnt matchng score dstrbuton s mnmum. Fgure 12 and 13 llustrate the best Beta-Bnomal dstrbuton plots, correspondng to mnmum error, from the varous palmprnt matchng scores. The parameters of Beta-Bnomal functon n these fgures are n the followng order: ( α, β, n ). The table 3 summarzes the error for the best ft parameters usng the four models. It can be observed that for the genune matchng score dstrbuton; the Beta-Bnomal dstrbuton generates the mnmum error and hence offers the best model for palmprnt feature representatons 589
obtaned from the same palm matchng. The best ft error obtaned from the mposter matchng scores s mnmum when the Beta-Bnomal dstrbuton s employed. However, the error for the mposter scores obtaned from the PalmCode features s an excepton n ths as Beta dstrbuton acheves margnally better error dstrbuton error for the best ft mposter. The best set of expermental results (fgure 7, table 2) are obtaned from the CompCode features, for whch the Beta-Bnomal dstrbuton acheved the best ft. In summary, the expermental results suggest that the Beta-Bnomal dstrbuton acheved the mnmum error n most palmprnt feature dstrbutons, both for genune and mposter matches, and therefore more approprate model for the palmprnt score dstrbutons. 5. Conclusons Ths paper has nvestgated a new approach to acheve the performance mprovement for tradtonal palmprnt authentcaton approaches presented n the lterature. The performance mprovement s acheved by ntegratng cohort nformaton n the decson makng. The cohort nformaton s used n the matchng stage only when the matchng scores are nadequate to generate relable decsons. The rgorous expermental results presented n ths paper from the peg-free hand database and PolyU palmprnt database llustrate sgnfcant mprovement n the performance,.e., decrease n equal error rate by 78.57% for approach n [4], 60.47% for the approach n [6], and 85.39% for the approach detaled n [5]. Although the performance mprovement s consstently sgnfcant, t comes wth some added computatonal complexty but only for false rejects. The classfer has to perform addtonal matchng operatons, wth all the mposters, every tme the resultng matchng score from the genune user s more than the fxed decson threshold. An mportant aspect of ths work s on the estmaton of the accuracy of varous theoretcal models for the palmprnt matchng scores. The rgorous expermental results from over 29.96 mllon comparsons on the publcly avalable database suggest that the Beta-Bnomal dstrbuton s the most approprate model for the palmprnt score characterzaton. Ths paper, for the frst tme n the bometrcs lterature, has presented expermental results from the touchless and peg-free palmprnt database. The new touchless database [15] has sgnfcantly hgher ntra-class varatons (translaton, scale, and orentaton) and s now freely/publcly made avalable for further palmprnt research and development. The expermental results llustrated n ths paper from the IITD palmprnt database are from rght hand mages whle results from the left hand mage also acheved sgnfcant performance mprovement, but could not be ncluded (also the matchng score dstrbutons) due space constrants n ths paper. 6. Acknowledgement Ths work was partally supported by the research grant from Department of Scence and Technology, Government of Inda (grant no. 100/IFD/1275/2006-2007). Author thankfully acknowledges the support of Mr. Anshu Vad and all the volunteers n acqurng the touchless palmprnt mages for IITD palmprnt database. 7. References [1] P. H. Hennngs-Yeomans, B. V. K. Kumar, and M. Savvdes,, Palmprnt classfcaton usng multple advanced correlaton flters and palm-specfc segmentaton, IEEE Trans. Info Forenscs & Securty, vol. 2, no. 3, pp. 613-622, Sep. 2007. [2] A. K. Jan and M. Demrkus, On latent palmprnt matchng, MSU Techncal Report, May 2008. [3] A. Kumar and D. Zhang, Personal recognton usng shape and texture, IEEE Trans. Image Process., vol. 15, no 8, pp. 2454-2461, Aug. 2006. [4] D. Zhang, W. K. Kong, J. You, and M. Wong, On-lne palmprnt dentfcaton, IEEE Trans. Patt. Anal. Machne Intell., vol. 25, pp. 1041-1050, Sep. 2003. [5] Z. Sun, T. Tan, Y. Yang, and S. Z. L, Ordnal palmprnt representaton for personal dentfcaton, Proc. CVPR 2005, pp. 279-284, 2005. [6] W. K. Kong and D. Zhang, Compettve codng scheme for palmprnt verfcaton, Proc. ICPR 2004, pp. 520-523, 2004, [7] A. K. Jan, A. Ross, and S. Pankant, A Prototype hand geometry-based verfcaton system, Proc. of 2nd Internatnal Conference on Audo and Vdeo-Based Bometrc Person Authentcaton, Washngton DC, pp.166-171, Mar 1999. [8] J. Daugman, The mportance of beng random: Statstcal prncples of rs recognton, Pattern Recognton, vol. 36, no. 2, pp. 279-291, 2003. [9] S. Pankant, S. Prabhakar, and A. K. Jan On the Indvdualty of Fngerprnts, IEEE Trans. Pattern Analyss and Machne Intellgence, vol. 24, no. 8, pp. 1010-1025, 2002. [10] A. Kumar and D. Zhang, Hand geometry recognton usng entropy-based dscretzaton, IEEE Trans. Info. Securty Forenscs, vol. 2, pp. 181-187, Jun. 2007. [11] N. L. Johnson, A. W. Kemp, and S. Kotz, Unvarate Dscrete Dstrbutons, 3 rd edton, New York, Wley, 2005. [12] The PolyU Palmprnt Database (verson 2.0); http://www.comp.polyu.edu.hk/~bometrcs [13] M. E. Schuckers, Usng the beta-bnomal dstrbuton to access performance of a bometrc dentfcaton devce, Int. J. Image & Graphcs, vol. 3, no. 3, pp. 523-529, 2003. [14] E. T. Bradlow, P. J. Everson, Bayesan nference for the Beta-Bnomal dstrbuton va polynomal expansons, J. Comput. & Graphcal Statstcs, vol. 11, no. 1, pp. 200-207, Mar. 2002. [15] IITD Palmprnt Database, http://web.td.ac.n/~ajaykr/database_palm.htm 590