Novel Pattern-based Fingerprint Recognition Technique Using 2D Wavelet Decomposition

Transcription

1 Mathematcal Methods for Informaton Scence and Economcs Novel Pattern-based Fngerprnt Recognton Technque Usng D Wavelet Decomposton TUDOR BARBU Insttute of Computer Scence of the Romanan Academy T. Codrescu,, cod 70048, Ias ROMANIA tudbar@t.tuas.ro Abstract: - We ntroduce a supervsed pattern-based fngerprnt matchng approach n ths artcle. Our proposed approach apples a D Wavelet decomposton n the feature extracton stage. The fngerprnt feature vectors resulted from our block-based featurng process are then classfed usng a tranng set. Next, an automatc threshold-based fngerprnt verfcaton procedure s then successfully appled on the dentfed dgtal fngerprnts. Key-Words: - Bometrc authentcaton, pattern-based fngerprnt recognton, D Dscrete Wavelet Transform, Wavelet decomposton, feature vector, supervsed classfcaton, automatc fngerprnt verfcaton. Introducton In ths paper we approach an mportant person recognton doman []. Fngerprnts are one of many forms of bometrcs used for person dentfcaton and verfcaton. Automatc fngerprnt recognton represents the most used bometrc authentcaton technque [,]. Person authentcaton by dgtal fngerprnts s very popular because of the unqueness of these bometrc dentfers, ther consstency over tme, the nherent ease n fngerprnt aquston and the hgh collectablty of the fngerprnts (because the human fngers representng numerous sources avalable for collecton) [3]. There are numerous mportant applcaton areas of the fngerprnt recognton systems. The most mportant are controllng the access to varous servces and the law enforcement [3]. The fngerprnt recognton approaches are dvded nto two categores: mnutae based [4] and pattern based authentcaton technques [5-7]. We consder a pattern-based fngerprnt recognton approach n ths artcle. The pattern-based authentcaton algorthms compare the nput fngerprnt mages wth some regstered fngerprnt patterns. Ths s done by regsterng dgtal fngerprnt mages based on a so called core pont dentfed as a reference pont n the pattern of fngerprnts [8]. Then the fngerprnt mage s globally represented by usng D Gabor flters [5-7], Fourer descrptors, Wavelet transforms [6,9] or the quantfed co-snusodal trplets. Our approach s based on the two-dmensonal Dscrete Wavelet Transforms (D-DWT) [6,9-]. The proposed Wavelet-based fngerprnt feature extracton technque s detaled n the next secton. Then, a fngerprnt dentfcaton approach, based on a supervsed feature vector classfcaton algorthm, and a fngerprnt verfcaton method, usng an automatc threshold detecton, are provded n the thrd secton. We have performed a lot of numercal experments usng the proposed approach, whch are mentoned n the fourth secton. Ths artcle ends wth a conclusons secton and wth a lst of references. Wavelet based Fngerprnt Feature Extracton The wavelet features are used by certan mnutae based recognton methods. Whle mnutae-based fngerprnt authentcaton uses both D and D wavelet analyss [], pattern based fngerprnt recognton uses the two-dmensonal wavelet transforms only [6,9-]. The D Dscrete Wavelet Transform (D DWT) s a mult-resoluton analyss technque for twodmensonal sgnals. Applyng a mult-level D DWT decomposton on a dgtal mage produces ts converson from the spatal doman nto the ISBN:

2 Mathematcal Methods for Informaton Scence and Economcs frequency doman, whle provdng a seres of submages known also as sub-bands [0]. The mplementaton of the Dscrete Wavelet Transform s performed by usng two flters for D sgnal processng: a low-pass flter and a hgh-pass flter. The dscrete wavelet analyss passes the twodmensonal sgnal through these two complementary flters, the result of ths processng beng two D dstnct sgnals. The frst flter extracts the low frequency components, the most mportant ones, known as approxmatons of the D sgnals. The second flter extracts the hgh frequency components, known as the detals of such sgnals. In Fgure one descrbes the block dagram of the decomposton of a two-dmensonal dscrete sgnal S on the bass of these two types of dgtal flters. The two acheved component sgnals are A, contanng the approxmatons of the sgnal S, respectvely D, contanng the detals of the same sgnal [9]. The decomposton process may contnue teratvely on several levels, the approxmatons component sgnal A beng the one reprocessed through DWT. At each level the approxmatons sgnal s fltered and decomposed n the two D sgnals of lower resoluton, and so on. Fgure. Wavelet decomposton of a sgnal The wavelet decomposton tree correspondng to an mage I s schematcally represented n Fgure. In ths case one can see 3 decomposton levels. Fgure. DWT decomposton tree of a dgtal mage I The fngerprnts can be the subect of such DWT decompostons n the process of ther recognton. In Fgure 3 we present an example of Dscrete Wavelet Transformaton on 4 levels for a fngerprnt mage. In order to obtan better recognton results, a preprocessng stage of the fngerprnt dgtal mage s needed, ts goals beng to mprove the contrast, to extract the nterest regon and to detect the reference pont. After that t s possble to construct a feature vector on the bass of the wavelet characterstcs of the preprocessed mage. The reference pont, or core pont, of the fngerprnt, wll be used as the center of the feature map correspondng to that fngerprnt [8]. It s defned as the pont where the curvature of the fngerprnt rdge s the most accentuated. Thus, one constructs an orentaton map of all rdges n the fngerprnt mage and then on ths map we detect the pxels for whch ther orentaton s more dfferent than the one of ts neghbors. Therefore, n our approach, we cut a [ M N] rectangular regon nsde the fngerprnt dgtal mage F, centered on the dentfed reference pont. The obtaned mage s then dvded n nonoverlappng [ K K ] blocks. Let { B,..., B n } be the sequence of resulted blocks [9]. For each B block n ths sequence, we apply the mult-resoluton D-DWT analyss down to the k level. So, we start wth A 0 = B, and on each level the current block s decomposed n the submages A and A D, whch correspond to the ISBN:

3 Mathematcal Methods for Informaton Scence and Economcs approxmatons and, respectvely, to the detals. From here on we take nto consderaton only the sub-mages correspondng to the resulted detals,.e. { D,..., Dk }, and for each of them we compute the normalzed energy. The sequence of the k values obtaned n such a way represents the feature vector of the block B. The feature vector of the fngerprnt results by concatenatng the vectors belongng to each component block, and ths s done by placng them on the rows of a matrx. We can model the wavelet-based feature extracton for the F fngerprnt dgtal mage n the followng form: where [, ] V ( B )[ ], [, n], [ k] V ( F) =, () V ( B ) D [ ] =, [, n], [ k] k Dt t=, () and represents the Eucldean norm of the sgnal taken as an argument. The fngerprnt feature vector V(F), that results from (), has a [ n k] dmenson. The obtaned D feature vector has dscrmnaton power between fngerprnts because t approxmates the energy of the fngerprnt mage on several levels. The dstances between these fngerprnt feature vectors can be compared usng some well-known metrcs, such as the Eucldean dstance (and ts varants) or SAD (sum of absolute dfferences). 3 Fngerprnt Classfcaton and Verfcaton Models The fngerprnt dentfcaton s performed by a supervsed classfcaton procedure appled to the wavelet feature vectors, based on matchng the nput fngerprnts to the patterns of a tranng set of fngerprnts [9]. In our approach, fngerprnt recognton s done n a supervsed manner, whch can be formalzed as follows. So, let us consder a sequence of nput dgtal fngerprnts F,..., F } whch have to be { n dentfed. The tranng set of the bometrc system s composed of the fngerprnts of N authorzed persons (fngerprnt patterns). So, the tranng set has the form of { Fn } }, where Fn =,..., ) =,..., N represents the th pattern of the th authorzed person, and ) returns the number of fngerprnts regstered for that person [9]. We propose a mnmum average dstance based supervsed classfer, representng an extended varant of the mnmum dstance classfcaton, for the fngerprnt dentfcaton purpose [3]. The class of the current nput fngerprnt F corresponds to an assocated regstered person and t s noted as C nd ( ), where ts ndex s determned accordng to the followng relatonshp: nd( ) = arg mn ) k = [, N ] d( V ( F ), V ( Fnk )), ) [, n] (3) where d s a proper metrc (lke the Eucldan dstance or the SAD). The obtaned N fngerprnt classes C,...,C, N represent the dentfcaton result. The next step of bometrc authentcaton conssts of a fngerprnt verfcaton process. The verfcaton role s to valdate or nvaldate the performed fngerprnt dentfcaton. The verfcaton of the fngerprnt denttes s performed through a threshold-based process modeled n the followng form: ) d( V ( F), V ( Fn )) = [, N], F C : > T C = C \ { F} (4) ) Fgure 3. The DWT decomposton of a fngerprnt mage where T represents an automatcally computed threshold value [9]. Thus, t s determned as overall maxmum dstance between any two tranng feature ISBN:

4 Mathematcal Methods for Informaton Scence and Economcs vectors belongng to the same regstered user. Therefore, the proper threshold wll be computed as followng: N k t ) T = max max d( V ( Fn ), V ( Fn )) (5) k= Accordng to (4), an already dentfed fngerprnt s excluded from the class of a recorded person f the average dstance to the patterns of that person s not suffcently small. The excluded fngerprnts are labeled as unauthorzed by the recognton system. 4 Numercal Experments The fngerprnt authentcaton technque proposed n ths paper has been tested on many fngerprnt datasets. The numerous experments that have been performed produced satsfactory results. A hgh fngerprnt recognton rate, of approxmately 85%, has been acheved. Hgh values have been obtaned for Precson and Recall parameters. Thus, we have used a database contanng 40 fngerprnt mages of sze 300 by 600 pxels, ncludng 0 mages per fnger of 5 ndvduals. These mages have been selected from the database based on the fngerprnts pattern nsde the mage, beng preferred those where the reference pont s located close to the mage center. The poor qualty mages or those havng the core pont too close to ther margns have been reected. The reference pont detecton process runs qute fast, ts executon takng approxmately.5 seconds. The detecton algorthm has a complexty of O(n ). Method comparsons have been also performed. We have compared ths Wavelet-based approach wth other pattern-based fngerprnt matchng technques. We have found that the DWT based method performs better and also runs much faster than some fngerprnt authentcaton algorthms usng D Gabor flterng. Thus, a D Gabor flter-based recognton technque proposed n our prevous works has a processng tme of 8 seconds for feature map extracton, whle the method provded here performs the feature extracton n less than 0 seconds. Also, the Wavelet-based technque obtans a hgher fngerprnt recognton rate than D Gabor flter-based authentcaton approaches. Ths patternbased authentcaton approach outperforms some mnuta-based fngerprnt recognton algorthms too. k t 5 Conclusons A novel pattern matchng based fngerprnt recognton system has been provded n ths paper. Our artcle brngs maor contrbutons n both dentfcaton and verfcaton stages of ths bometrc process. Obvously, the proposed D Wavelet decomposton based fngerprnt feature extracton represents the man contrbuton of ths artcle. Our approach produces robust D fngerprnt feature vectors. A supervsed mnmum average dstance based classfer usng a fngerprnt tranng set has been proposed for feature vector classfcaton. The dentfcaton result was then valdated through a robust threshold-based fngerprnt verfcaton process. The automatc threshold detecton approach s another novel contrbuton of ths paper. The satsfactory results of the recognton tests performed by applyng ths proposed approach on large fngerprnt databases, and also the method comparsons performed, prove the effectveness of our Wavelet-based authentcaton method. The recognton technque descrbed here could be successfully appled n mportant areas, such as: access control, law enforcement, and dgtal fngerprnt database ndexng and retreval [4,5] respectvely. Our future works wll focus on combnng the pattern-based fngerprnt matchng approaches. Thus, a much more powerful fngerprnt recognton system can be developed by correlatng the DWTbased and Gabor flter-based authentcaton technques [6,9]. Also, the possblty to ntegrate our fngerprnt recognton approach nto a more complex multmodal bometrc authentcaton system [9] wll also be nvestgated durng our future research. Acknowledgments The research work descrbed here has been supported by the proect PN-II-PCE , fnanced by the Romanan Mnster of Educaton and Technology. We acknowledge also the research support of the Insttute of Computer Scence of Romanan Academy, Ias branch, Romana. References: [] Jan, A. K., Ross, A., Prabhakar, An ntroducton to bometrc recognton In IEEE Transactons on Crcuts and Systems for Vdeo ISBN:

5 Mathematcal Methods for Informaton Scence and Economcs Technology, Volume 4, Issue, 004, pp [] Jan, L.C. et al. (Eds.), Intellgent Bometrc Technques n Fngerprnt and Face Recognton, Boca Raton, FL: CRC Press, 999. [3] rec.pdf, NSTC Subcommttee on Bometrcs, Fngerprnt recognton, 006. [4] Rav, J., Raa, K. B., Venugopal, K. R., Fngerprnt Recognton Usng Mnuta Score Matchng, Internatonal Journal of Engneerng Scence and Technology, Vol. (), 009, pp [5] Munr, M. U., Javed, M. Y. Fngerprnt Matchng usng Gabor flters, Natonal Conference on Emergng Technologes, 004. [6] Tudosa, A., Costn, M., Barbu, T., Fngerprnt Recognton usng Gabor flters and Wavelet Features, Scentfc Bulletn of the Poltehnc Unversty of Tmsoara, Romana, Transactons on Electroncs and Communcatons, Tom (49) 63, Fasc., 004, pp [7] Barbu, T., Costn, M., Cobanu, A., Patternbased Fngerprnt Matchng Approach, Proceedngs of Internatonal Workshop on Intellgent Informaton Systems, IIS 0, pp , -4 September 0. [8] Park, J., Hanscok, K. Robust reference Pont Detecton Usng Gradent of Fngerprnt Drecton and Feature Extracton Method, Computatonal Scence ICCS 003, Part, 003. [9] Barbu, T., Bometrc Authentcaton Technques (n Romanan), The Publshng House of the Romanan Academy, 0 pages, 0. [0] Mallat, S., A wavelet tour of sgnal processng, Academc Press, San Dego, Calforna, USA, 998. [] Tco, M., Kuosmanen, P., Saarnen, J. Wavelet doman features for fngerprnt recognton, n Electroncs Letters, Volume 37, no., 00. [] Dale, M. P., Josh, M. A., Fngerprnt matchng usng transform features, TENCON 008, IEEE Regon 0 Conference, 008. [3] Wacker, A. G. Mnmum Dstance Approach to Classfcaton, Ph.D. Thess, Purdue Unversty, Lafayette, Indana, January, 97. [4] Boer, J., Bazen, A., Cerez, S., Indexng fngerprnt database based on multple features, In Proceedngs of the ProRISC 00 Workshop on Crcuts, Systems and Sgnal Processng, 00. [5] He, S., Chao Zhang, C., Hao, P., Clusterng- Based Descrptors for Fngerprnt Indexng and Fast Retreval, In Proceedngs of the 9th Asan Conference on Computer Vson, ACCV 009, X'an, Chna, pp , September 3-7, 009. ISBN: