Off line signatures Recognition System using Discrete Cosine Transform and VQ/HMM

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1 Ausralan Journal of Basc and Appled Scences, 6(12): , 2012 ISSN Off lne sgnaures Recognon Sysem usng Dscree Cosne Transform and VQ/HMM 1 Behrouz Vasegh, 2 Somayeh Hashem, 1,2 Deparmen of Elecrcal Engneerng of Islamc Azad Unversy, Abhar Branch Abhar, Iran. Absrac: Ths paper presens a new recognon sysem for offlne sgnaures usng Dscree Cosne Transform (DCT) and pseudo 2 Dmenson Dscree Hdden Markov Model (P2D-DHMM). Each sgnaure mage s scanned n wo ways. One way from rgh o lef and one way from op o boom by a sldng wndow and wo ses of feaures are exraced. 2D-DCT coeffcens as feaures are exraced. K-means cluserng s used for generaon wo codebooks and hen by he vecor quanzaon (VQ) wo code words for each sgnaure mage are generaed. These code words are used as observaon vecors n ranng and recognon phase. Two separae Dscree HMM (each HMM for each way) s raned by Baum Welch algorhm for each se of conanng mage of he same sgnaure ( v h λ c, λ c ). A es sgnaure mage s recognzed by fndng he bes mach (lkelhood) beween he mage and all of v h he HMMs ( λ c + λ c ) sgnaure models usng forward algorhm. Expermenal resuls show he advanages of usng P2D-DHMM recognzer engne. Key words: Dscree Cosne Transform; Hdden Markov Model; K-means algorhm. INTRODUCTION Bomerc recognon has been descrbed as auomac denfcaon of an ndvdual based on physologcal and behavoral characerscs. Bomercs ras (sgnaure, voce, rs, fngerprn ec.) are preferred o radonal mehods (namely passwords, PIN numbers, smarcards ec.) because bomerc characerscs of ndvdual are no easly ransferable; hey are unque and canno be solen. Whn bomerc mehods, auomacs sgnaure recognon s an mporan research area because of he socal, legal and wder accepance of handwren sgnaure as means of denfcaon. Sgnaure recognon sysems can generally dvded no wo classes called onlne and offlne. In an offlne echnque, sgnaure s sgned on a pece of paper and scanned o a compuer sysem. In an on-lne echnque, sgnaure s sgned on a dgzer and dynamc nformaon lke speed, pressure s capured n addon o mage of he sgnaure. Recognon decson s usually based on local or global feaures exraced from sgnaure under processng. Excellen recognon resuls can be acheved by comparng he robus model of he query sgnaure wh all he user models usng approprae classfer. Sgnaure recognon sysem can be descrbed as a wo-class classfcaon sysem, he classes are genune and forgery. The am of any sgnaure recognon/verfcaon sysem s o deec one or more caegory of sgnaure forgeres namely random, smple, and sklled. Random or zero-effec forgery s any scrbbled wren sgnaure of genune sgnaure of anoher person. Smple or casual sgnaure forgery s forged by a forger who s famlar wh he name of he genune user bu has no access o hs/her sgnaure samples. Sklled sgnaure forgery s forged by a forger who has unresrced access o one or more sgnaure samples of he genune user. The performance of a sgnaure verfcaon or recognon sysem s generally evaluaed accordng o he error represenaon of a wo-class paern recognon problem, he error represenaons are False Rejeced Rao (FRR) and False Accepance Rao (FAR). (F. Leclerc and R. Plamondon. 1994; R. Sabourn. 1997; J. P. Edward. 2002). An effecve sgnaure recognon sysem mus have hgh recognon rae. Recognon accuracy depends on he ably of he sysem o reduce nra varaon whn he sgnaures of he same person whle ncrease he ner varaon beween sgnaures of dfferen people. And hs depends on echnques adoped n ranng and classfcaon of sgnaures. I also depends on he exraced feaures. Many researchers have used combnaon of dfferen feaures and classfers o developed sgnaure recognon sysems. Among varous sochasc approaches, HMMs have proven very effecve n modelng boh dynamc and sac sgnals (J. Coezer, 2004; V.V Kohr and U.B. Desa. 1998; V. Kan, 2010; E. Yacoub, 2000). Prevous HMM based sgnaure recognon sysems used connuous HMM opology, dfferen number of saes for users and weak feaures for ranng and classfcaon of sgnaure mages (J. Coezer, 2004; E. Yacoub, 2000; E. Jusno, 2001; E. Jusno, 2005; E. Jusno, 2000). In hs paper, combnaon of 2D-DCT sgnaure feaures and dscree HMM are ncorporaed o develop a robus model framework and sgnaure classfcaon algorhm. Secon 2 provdes he descrpon of he sysem, he preprocessng, frame generaon, and feaure exracon echnque, Cluserng and quanzaon. Hdden Markov Model and Recognon are gven n secon 3. Fnally Expermenal resuls and Concluson presened n secon4. Correspondng Auhor: Behrouz Vasegh, Deparmen of Elecrcal Engneerng of Islamc Azad Unversy, Abhar Branch Abhar, Iran. E-mal: Behrouz.vasegh@gmal.com 423

2 2. The Sgnaure Recognon Sysem: Off-lne sgnaure recognon sysem proposed n hs paper s bascally dvded no sx sages namely, daa acquson, preprocessng, frame generaon, feaure exracon, cluserng and vecor quanzaon, ranng and recognon sages as shown n Fgure 1. Fg. 1: An overvew of proposed sgnaure recognon sysem. 2.1.Inpu Sgnaure Daa: The npu daa o he proposed sysem are genune sgnaures of he regsered users. Genune sgnaures are colleced from 50 men and 50 women ha each of he men and women conrbued 15 genune sgnaure samples. The sgnaure samples are scanned by a scanner wh 300-dp resoluon and n 256 gray levels. Therefore we have 1500 sgnaure mages n our daabase. 2.2.Preprocessng: The preprocessng consss of he followng seps: Bnarzaon: The gray level mage of a sgnaure s bnarzed a a hreshold deermned by modfed verson of maxmum enropy sum and enropc correlaon mehods. (Fgure 2-2). Nose removal: The bnarzed mage ofen has spurous segmens whch are removed by a morphologcal closng operaon followed by a morphologcal openng operaon boh wh a 3x3 wndow as he srucure elemen. (Fgure 2-3). To Surround: For decrease of he memory volume and ncrease he speed of he processng he bnarzed mage s surrounded n a crcumferenal recangular (Fgure 2-4). Fg. 2: An example mage durng preprocessng. 2.3.Frame Generaon and Feaure Exracon: In hs phase, he sgnaure mage s convered o an approprae sequenal form suable for HMM recognon engne.the area of he mage s dvded no a se of vercal and horzonal fxed-wdh frames (srps) from rgh o lef and op o boom. The wdh of a frame s se o approxmaely wce of he average 424

3 sroke wdh of he sgnaure mage and here s a 50% overlap beween wo consecuve frames. From each frame 2D-DCT coeffcens are calculaed and 9 hgher coeffcens (3*3 arrays) for generaon he observaon vecor are seleced.in hese way wo ses of observaon vecors (one se from vercal scannng and one se from horzonal scannng) are generaon. 2.4.Cluserng and Quanzaon: A se of feaure vecors exraced ( V = { v1, v2,, vn} ) from more han 100,000 sgnaure srps (frames) were gahered o generae a codebook. The K-means algorhm s used o segmen hs vecor space no C-parons represened by a se of cluser ceners (A.K. Jan, 1999). Afer generang he codebook, a gven feaure vecor ( v ) s mapped no a membershp vecor U = [ u 1, u2,..., uc ]. Thus, each sgnaure mage, represened by a sequence of feaure vecors exraced from sgnaure frame, s now mapped no an observaon sequence of membershp vecors (nsead of an observaon sequence of sngle values n he case of convenonal VQ/HMM). Therefore, he Baum Welch Re-esmaon algorhm (n ranng phase) and Forward algorhm (n recognon phase) mus be used o ake no accoun hs observaon sequences. 3. Hdden Markov Model: In hs mehod, each sgnaure class s modeled by wo sngle rgh-lef HMM. A HMM ( λ c ), s defned by he followng parameer: (Mkael Nlsson). The number of saes (N) whch s se for each class s proporonal o he average numbers of frames n ranng samples n ha class. The ndvdual saes are denoed as: S = (1) S, S,, S { 1 2 N and he sae a me as q. } The number of dsnc observaon symbols per sae (M), whch s se equal o 10 n hs case. we denoe he ndvdual symbols as V = (2) { v, v2,, v 1 M } Whch are c cluser ceners obaned by K- means cluserng algorhm. The sae ranson probably dsrbuon: A = { a j } (3) Tha a = p q + / q = s ], 1, j N (4) j And [ 1 a = 0 f ( j ) or( j + ) j (5) The maxmum number of forward jumps n each sae ( ) s chosen expermenally o be beween 2 and 4 for each class durng ranng. The observaon symbol probably dsrbuon : B Tha = (6) { b ( m)} j 425

4 b m) = p[ v a / q = s ] 1 j N, 1 m M j ( m j The nal sae dsrbuon :, (7) Π = π } {, 1 N (8) Tha = p[ q 0, 1 = s ] = 1, = 1 π (9) The las sae dsrbuon : Γ = γ } 1 N Tha = { p[ q T, (10) 0, N = s ] = 1, = N γ (11) The se of K observaon sequences (ranng samples) for each sgnaure class: ( 1) (2) O = { O, O,... O } (12) Tha (1) (2) O = { O, O,... } (13) And 1 2 OT K (K ) O s he observaon vecor a frame n he Kh ranng sample. In hs way each sgnaure mage n he es daa se s represened as wo sequences of T, observaons and for each sgnaure mage se, wo separae rgh-lef DHMM s raned by hese observaons and Baum- Welch algorhm. The dsrbuons Π and Γ are no re-esmaed snce hey are predefned n rgh-lef HMM as show n equaons (5-10). Afer ranng all of he HMMs by Baum-Welch algorhm he probably ha o has been generaed by each sgnaure model, ( P( o λ ),1 < h < 50 and P( o ),1 < v < 50 h λ ) was compued by forward v algorhm as follows: The forward varable for a gven sgnaure mage sample K s calculaed as: T h v α k π j. bj.( o ), = 1 ( j) = N ( k ) ( α 1 ( ). aj bj ( O = 1 k ) ), 2 T k,1 j N (14) Smlarly, he backward varable for gven sgnaure mage sample K s calculaed as: 426

5 ( k ) γ j, = TK ( j) = N ( a jbj ( O = 1 k ) ) β ( k ) + 1 ( ), = T K,1 1,...,1 β (15) j N P K, h P( O h ) = = λ α ( ). γ (16) TK P K, v P( O v ) = = (17) λ α ( ). γ TK Fnally he observaon probably s calculaed as: P K PK, h + PK, v = (18) And a sored ls of canddae classes s obaned. 4. Exprmenal Resuls and Concluson: 1500 sgnaure mages of 100 men and women ha were colleced n a daabase and used for developng paern recognon. Afer applyng preprocessng seps ncludng bnarzaon, nose removal and beseged n a crcumferenal recangular each sgnaure mage s scanned from rgh o lef and op o boom by a sldng wndow and from each wndow 2D-DCT feaures s exraced. A codebook conssng 10 codeword s consruced usng K-means cluserng mehod from a pool of abou feaure vecors exraced from sgnaure mages. By usng hs codebook each sgnaure s represened as a sequence of membershp vecors. For each men/women sgnaures wo separae rgh-lef HMM are raned by Baum-Welch algorhm. In recognon phase he probably of generang he es mage by each HMM s compued by forward algorhm. (by equ. 16,17 and 18) and a sored ls of canddae class s obaned. For compung he recognon rae hree dsnc ses (A, B, C) ha each se conan 5 sgnaure mages are predefned n he daabase usable for ranng and esng sysem. We use wo of hem for ranng and one se for esng and he mean of recognon rae n each condon s consdered as recognon rae of he sysem n ha condon. Performance of he proposed sgnaure recognon sysem s shown n able 1. Table 1: Recognon rae of proposed sysem. Tes Tranng ses Tes ses Recognzed No Recognzed (FAR) 1 A,B C 93.14% 6.86% 2 B,C A 91.1% 8.9% 3 A,C B 92.75% 7.25% Mean of recognon rae 92.33% 7.67% In able 2 he performance of he propose sgnaure recognon sysem s compared wh he oher recognon sysems. Table 2: comparsons o oher sgnaure recognon sysem. Mehod Recognzed No Recognzed (FAR) [14] 92% 8% Proposed sysem 92.33% 7.67% [15] 92.5% 7.5% [16] 99.1% 0.9% Examnaons our resuls and comparson wh he connuous HMM show he performance of propose sysem n sgnaure mage recognon. In hs work s used from a basc concep of HMM and avoded from he complex mehods and algorhms. These are advanage for hs work. REFERENCES Plamondon, R. and S.N. Srhar, On-lne and off-lne Handwrng Recognon: A comprehensve Survey, IEE ran. on Paern Analyss and Machne Inellgence, 22(1):

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