Automated System for Criminal Identification Using Fingerprint Clue Found at Crime Scene

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1 Volume 2, Issue 11, November 213 ISSN Automated System for Crmnal Identfcaton Usng Fngerprnt Clue Found at Crme Scene Mansh P.Deshmukh 1, Prof (Dr) Pradeep M.Patl 2 1 Assocate Professor, E&TC Department, SSBT s COET, Bambhor, Jalgaon 2 Drector, RMD STIC, Warje, Pune - 58 Abstract Crmnal dentfcaton based on explct detecton of complete rdge structures n the fngerprnt s dffcult to extract automatcally. Local rdge structures cannot be completely characterzed by mnutae. Further, a mnutae based matchng has dffculty n quckly matchng two fngerprnt mages contanng dfferent number of unregstered mnutae ponts. The proposed matchng algorthm that uses both mnutae (pont) nformaton and the texture (regon) nformaton s the soluton n that drecton. The algorthm uses a bank of Gabor flters to capture both local and global detals n a fngerprnt as a compact fxed length fnger code. The fnger prnt matchng s acheved wth the help of Eucldean dstance between the two correspondng fnger-codes and hence s extremely fast. Results obtaned wth the proposed scheme shows that a combnaton of mnutae and texture based score matchng (local as well as global) nformaton leads to a substantal mprovement n the overall matchng performance even at low resolutons. Keywords: Fngerprnt, Mnutae, Gabor Flter, Eucldan Dstance 1. INTRODUCTION Bometrcs, whch refers to dentfyng an ndvdual based on hs or her physologcal or behavoural characterstcs, has the capablty to relably dstngush between two persons. Among all the bometrcs (e.g. face, fngerprnts, hand geometry, rs, retna, sgnature voceprnt, facal thermo dagram, hand ven, gat, ear, odor, keystroke dynamcs, etc. [1, 2]), fngerprnt based Crmnal dentfcatons one of the most mature and proven technque and ganed mmense popularty due to the hgh level of unqueness attrbuted to fngerprnts.recently, due to the advancement and avalablty of compact sold state sensors that can be easly embedded nto a wde varety of devces have facltated use of Automated System for Crmnal Identfcaton based on the fngerprnts found at the place of crme. A fngerprnt s the pattern of rdges and valleys on the surface of the fnger [3]. The unqueness of fngerprnts can be determned by the overall pattern of rdges and valleys as well as the local rdge anomales (a rdge bfurcaton or rdge endng, called mnutae ponts). Although the fngerprnt possesses the dscrmnatory nformaton, desgnng a relable automatc fngerprnt-matchng algorthm s very challengng as mages of two dfferent fngers may have the same global confguraton. As fngerprnt sensors are becomng smaller and cheaper [4], automatc dentfcaton based on fngerprnts s becomng an attractve alternate / complment to the tradtonal methods of dentfcaton. The crtcal factor on the wdespread use of fngerprnts s n satsfyng performance (e.g. matchng speed and accuracy) requrements of the emergng cvlan dentfcaton applcatons. Some of these applcatons (e.g. fngerprnt based smart cards) wll also beneft from a compact representaton of a fngerprnt. Wth the advent of lve scan fngerprntng and avalablty of cheap fngerprnt sensors, fngerprnts are ncreasngly used n government and commercal applcatons for postve person dentfcaton [5, 6]. In the lterature many schemes uses local landmarks.e. mnutae based fngerprnt matchng systems or exclusvely global nformaton. The mnutae based technques typcally match the two mnutae sets from two fngerprnts by frst algnng the two sets and then countng the number of mnutae that match. A typcal mnutae extracton technque performs the followng sequental operatons on the fngerprnt mage: ()fngerprnt mage enhancement, () bnarzaton (segmentaton nto rdges and valleys), () thnnng, and (v) mnutae detecton. Several commercal [1] and academc [7-9] algorthms follow these sequental steps for mnutae detecton. Number of researchers used the global pattern of rdges and furrows [1-12]. The smplest technque s to algn the two fngerprnt mages and subtract the nput from the template to see f the rdges correspond. However, such a smplstc approach suffers from many problems ncludng the errors n estmaton of algnment, non-lnear deformaton n fngerprnt mages, and nose. Combnng both local and global features gves mproved results n fngerprnt verfcaton [13-17]. So we have proposed an automated system for crmnal dentfcaton based on hybrd features of the fngerprnt mage found at the crme scene. 2. BLOCK DIAGRAM OF PROPOSED SYSTEM FOR FINGER PRINTS We descrbe a hybrd approach to dentfy the crmnal based on fngerprnt matchng that combnes a mnutae based representaton of the fngerprnt wth a Gabor flter (texture based) representaton for matchng purposes. In the proposed algorthm when a query mprnt s presented, the matchng proceeds as follows: () The query and template mnutae features are matched to generate mnutae matchng score and an affned transformaton that algns the query and Volume 2, Issue 11, November 213 Page 259

2 Volume 2, Issue 11, November 213 ISSN template fngerprnts. () Determne the reference pont (core pont) and tessellate the regon of nterest for the fngerprnt mage. () Compute the average absolute devaton(aad) from the mean of gray values n ndvdual sectors n fltered mages to defne the feature vector or the fnger code. (v) The query and template fnger codes are matched. (v) The mnutae and fnger code matchng scores are combned to generate a sngle matchng score (see Fgure1). Fgure 1 Hybrd approach for crmnal dentfcaton based on fngerprnt matchng 3. CORE POINT DETECTION 1. Dvde the nput mage, nto non-overlappng blocks of sze Compute the gradents x(, and y(, at each pxel (,. Dependng on the computatonal requrement, the gradent operator may vary from the smple Sobel operator to the more complex Marr-Hldreth operator. 3. Estmate the local orentaton of each block centered at pxel (, usng o 1 V (, 1 y, j tan 2 Vx(, (1) where, 4 j4 V x, j 2 x ( u, v) y ( u, v) (2) u4 v j4 4 j4 2 2 V y, j x ( u, v) y ( u, v) (3) u4 v j4 The value of o(, s least square estmate of the local rdge orentaton n the block centred at pxel (,. Mathematcally, t represents the drecton that s orthogonal to the domnant drecton of the Fourer spectrum of the 8 8 wndow. 4. Smooth the orentaton feld n a local neghbourhood. In order to perform smoothng (low pass flterng), the orentaton mage needs to be converted nto a contnuous vector feld, whch s defned as 1x, j cos 2 o(, (4) and, j sn 2 o(, ) 1y j (5) where, 1x and 1y, are the x and y components of the vector feld, respectvely. Wth the resultng vector feld, the low pass flterng can be performed as, w/ 2 w / 2 x, j W u, v 1 x wu, j wv (6) uw/ 2 vw/ 2 and Volume 2, Issue 11, November 213 Page 26

3 Volume 2, Issue 11, November 213 ISSN w / 2 w / 2 y, j W u, v 1 y wu, j wv (7) uw / 2 vw / 2 where, W (.) s a two dmensonal low pass flter wth unt ntegral and w w specfes the flter sze. Note that smoothng operaton s performed at the block level. For our expermentaton we have used a 5 5 mean flter. The smoothed orentaton feld O at (, s computed as, O 1 (, 1 y, j tan 2 x(, 5. Compute the sne component of the smoothed orentaton mage O, usng, j sn O (, E (9) (8) 6. Intalze R, a label mage used to ndcate the core pont. 7. For each pxel (, n E, compute the dfference n the pxel ntenstes of those pxels havng dfferent orentatons n O. 8. Fnd the maxmum value n R and assgn ts co-ordnates to the core. 4. REGION OF INTEREST EXTRACTION Let I x, y denote the gray level at pxel x, y n an M N fngerprnt mage and let x c, y c denote the core pont. The regon of nterest s defned by a collecton of sectors S, where the th sector S s computed n terms of parameters ( r, ) as follows: S where, x, y b T 1 r b T 2, 1, 1 x N, 1 y M T dv k (11) mod k 2 (12) x x y y 2 k 2 r ( ) c c (13) tan 1 y yc ( x xc ) (1) (14) bs the wdth of each band and k s the number of sectors consdered n each band. We use sx concentrc bands around the center pont. Each band s 18-pxels wde (b = 18), and segmented nto eght sectors (k = 8). The nnermost band s not used for feature extracton because the sectors n the regon near the center contan very few pxels. Thus, a total of sectors ( S through S39) are defned. 5. GABOR FILTERS USED FOR FINGERPRINT FEATURE EXTRACTION By applyng properly tuned Gabor flters to a fngerprnt mage, the true rdge and furrow structures can be greatly accentuated. These accentuated rdges and furrow structures consttute an effcent representaton of a fngerprnt mage. The general form of a 2D Gabor flter s defned by (6). A fngerprnt mage s decomposed nto eght component mages correspondng eght dfferent values of k = (,22.5,45,67.5,9,112.5,135 and ) wth respect to the x -axs. 6. IMPLEMENTED ALGORITHM In the proposed algorthm, the flter frequency f s set to the recprocal of the nter-rdge dstance snce most local rdge structures of fngerprnts come wth well-defned local frequency and orentatons. The average nter rdge dstance s approxmately 1 pxels n a 5 dp fngerprnt mage. If f s too large, spurous rdges may be created n the fltered mage, whereas f f s too small, nearby rdges may be merged nto one. The bandwdth of the Gabor flters s determned by x and y. If the values of x and y are too large, the flter s more robust to nose, but s more lkely to smooth the mage to the extent that the rdge and furrow detals n the fngerprnt are lost. On the other hand, f they are too small, the flter s not effectve n removng nose. In the proposed algorthm, the values of x and y were emprcally determned and both were set to 4. and the flter frequency f s set to.1. Volume 2, Issue 11, November 213 Page 261

4 Volume 2, Issue 11, November 213 ISSN Before decomposng the fngerprnt mage I x, y, normalze the regon of nterest x y N, n each sector separately to a constant mean and varance. Normalzaton s done to remove the effects of sensor nose and fnger pressure dfferences. Let I x, y denote the gray value at pxel x, y, M, and V, the estmated mean and varance of the sector S respectvely and N x, y, the normalzed gray-level value at pxel x, y. For all the pxels n sector S, the normalzed mage s 2 V I ( x, y) M M, f I ( x, y) M V (15) Nx, y 2 V (, ) I x y M M, otherwse, V where M and V are the desred mean and varance values, respectvely. Normalzaton s a pxel-wse operaton that doesn t change the clarty of the rdge and furrow structures. If normalzaton s done on the entre mage, then t cannot compensate for the ntensty varatons n the dfferent parts of the fnger due to fnger pressure dfferences. Normalzaton of each sector separately allevates ths problem. In the proposed algorthm, both M and V to a value were set to 1. After settng all the parameters of the Gabor flters, the even Gabor feature, at samplng pont ( X, Y) can be calculated usng, where G M 1 N 1 X, Y, k, f, x, y N X x, Y y g x, y, f, k, x, y (16) x y N.,. denotes a sector of normalzed fngerprnt mage x y I, of sze M N, havng 256 gray-levels. Fgure 2 (a)-(h)gabor features of fngerprnt mage for k (,22.5,45,67.5,9,112.5,135 and ) Fgure 3 (a) Orgnal mage (b) Tessellated mage (c) Reconstructed mage usng four Gabor flters (d) Reconstructed mage usng eght Gabor flters The magntude Gabor features at the sample pont and those of ts neghbourng ponts wthn three pxels are smlar, whle the others are not. Ths s because the magntude Gabor feature has the shft-nvarant property. A fngerprnt mage x y I, s thus normalzed and convolved wth each of the eght Gabor flters to produce eght component mages. Volume 2, Issue 11, November 213 Page 262

5 Volume 2, Issue 11, November 213 ISSN Convoluton wth an orented flter accentuates rdges parallel to the x -axs, and t smoothes rdges that are not parallel to the x -axs. Flters tuned to other drectons work n a smlar way. Accordng to the expermental results, the eght component mages capture most of the rdge drectonalty nformaton present n a fngerprnt mage (see Fgure 2) and thus form a vald representaton. It s llustrated by reconstructng a fngerprnt mage by addng together all the eght fltered mages. The reconstructed mage s smlar to the orgnal mage but the rdges have been enhanced. Fltered and reconstructed mages from four and eght flters for the fngerprnt are shown n Fgure 3 and Fgure 4. Fgure 4 (a) Orgnal mage (b) Tessellated mage (c) Reconstructed mage usng four Gabor flters (d) Reconstructed mage usng eght Gabor flters. 6.1 Mnutae Extracton Mnutae represent local rdge detals. Rdge endngs and rdge bfurcaton are the two popular mnutae used for fngerprnt matchng applcatons. A rdge bfurcaton s that pont on an mage where the rdge branches out nto two and rdge endng s the open end of the rdge. These features are unque for every other fngerprnt and are used for fngerprnt recognton. A template mage s created for all the detected rdge bfurcatons and rdge endngs n an mage after false rejecton as shown n Fgure 5. The mnutae matchng score s a measure of smlarty of the mnutae sets of the query and template mages. The smlarty score s normalzed n the [,1] range. (a) (b) Fgure 5 Mnutae set (a) querymageand (b) template mage 6.2 Fnger code generaton To generate the Gabor flter-based fnger code from the fngerprnt mage followng steps are performed sequentally as: Step 1:Fnd the core pont of each fngerprnt mage. Step 2:Tessellate the regon of nterest around the reference pont nto 4 sectors and sample the fngerprnt mage by set of Gabor flters to gve N k x, y, the fltered sectors of mage n k drectons. 1,2,3,...,4 and Step 3:Now, k (,22.5,45,67.5,9,112.5,135 and157.5 ) the feature values are the average absolute devaton from the mean defned as F 1 N ( x, y) P n (17) n where, n, s the number of pxels n the sector S, P s the mean of pxel values n the sector S. Volume 2, Issue 11, November 213 Page 263

6 Volume 2, Issue 11, November 213 ISSN Thus, the average absolute devaton of each sector of the eght fltered mages defnes the components (32) of the fnger code ( 8 58). The query and template fnger codes are then matched and the matchng score s found. The mnutae and fnger code matchng scores are then combned to generate a sngle matchng score. 7. EXPERIMENTAL RESULTS Although the fngerprnt databases of NIST, MSU, and FBI are sampled at 5 dp, the fngerprnt mages can be recognzed at 2 dp by the human eye. The recognton of low qualty mages s effcent and practcable for a smallscale fngerprnt recognton system. In the proposed system we have used a nked fngerprnt mage from the person (two mages) and captured the dgtal format wth a scanner at 2dp and 256 gray-level resolutons. The mnutae and fnger code s stored n the database as a template mage. The mnutae features are unque for every other fngerprnt and are used for fngerprnt recognton. Fgure 6 (a)-(h) Fnger codes for (,22.5,45,67.5,9,112.5,135 and ) () orgnal mage. k Fgure 7 (a)-(h) Fnger codes for (,22.5,45,67.5,9,112.5,135 and ) () orgnal mage. k Volume 2, Issue 11, November 213 Page 264 Fgure 6 and Fgure 7 shows fnger codes of two fngerprnts belongng to dfferent persons. From thesefgures we fnd that the fnger codes of dfferent persons do not match. Ths reveals that by usng both the mnutae and the fnger codes generated provdes more securty when t s used for crmnal dentfcaton usng fngerprnts found at the locaton of crme. The fngerprnt matchng s based on the Eucldean dstance between the two correspondng fnger codes and hence s extremely fast. Another experment was performed to fnd the Eucldan dstance between the test mage and rest mages of the same group. Table 1 shows the test results for 1 fngerprnt mages wth ts own ndvdual set of 8-dstracted mages. Ths dstracton was carred out wth respect to brghtness, contrast, partal cut of mages, blurrness, etc. The mplemented system results tabulated shows that f Eucldan dstance s equal to zero then the perfect match has been found else not. The test data shows that the Eucldan dstance and ts mean plays a vtal role n dentfyng any gven nput mage (latent) wth ts correspondng stored template mages (reference prnt). The mplemented system outperforms on the whole database. Table 1Computaton of Eucldan dstance and Mean usng the proposed algorthm Seral No Image ID Eucldan Dstance Mean of Eucldean Dstance 1 11_1 ( /8) = _ _ _ _

7 Volume 2, Issue 11, November 213 ISSN _ _ _ _ ( /8) = _ _ _ _ _ _ _ _ ( /8) = _ _ _ _ _ Seral No Image ID Eucldan Dstance Mean of Eucldean Dstance 13_ _ _ ( /8) = _ _ _ _ _ _ _ _ ( /8) = _ _ _ _ _ _ _ _ ( /8) = _ _ _ _ Seral No Image ID Eucldan Dstance Mean of Eucldean Dstance 16_ _ _ _ ( /8) = _ _ _ Volume 2, Issue 11, November 213 Page 265

8 Volume 2, Issue 11, November 213 ISSN _ _ _ _ _ ( /8) = _ _ _ _ _ _ _ _ ( /8) = _ _ _ Seral No Image ID Eucldan Dstance Mean of Eucldean Dstance 19_ _ _ _ _ ( /8) = _ _ _ _ _ _ Fgure 8 Enrollment of the fngerprnt mage from the subject. The Snapshots of the GUI for varous mprnts are provded below. Accept the fngerprnt mage from the subject usng a fngerprnt sensor.then enrolment of the fngerprnt mage from the subject n the form of feature vector s carred out as shown n Fgure 8. Matchng the query fngerprnt mage found at the crme scene wth the fngerprnt mages avalable n the database s then carred out as shown n Fgure 9. Volume 2, Issue 11, November 213 Page 266

9 Volume 2, Issue 11, November 213 ISSN Fgure 9 Matchngthe query fngerprnt mage found at the crme scene wth the fngerprnt mages avalable n the database. 8. CONCLUSION The proposed matchng algorthm that uses both mnutae (pont) nformaton and the texture (regon) nformaton s more accurate. Results obtaned on the fngerprnt captured n dgtal format wth a scanner at 2 dp and 256 gray level resolutons shows that a combnaton of mnutae based score matchng and texture based (local as well as global) nformaton leads to a substantal mprovement n the overall matchng performance. The flter frequency f and the values of x and y that determne the bandwdth of the Gabor flter should be selected properly. If f s too large, spurous rdges may be created n the fltered mage, whereas f f s too small, nearby rdges may be merged nto one. Smlarly, f the values of x and y are too large, the flter s more robust to nose, but s more lkely to smooth the mage to the extent that the rdge and furrow detals n the fngerprnt are lost. On the other hand, f they are too small, the flter s not effectve n removng nose. The fngerprnt matchng usng Eucldean dstance between the query and the template mage s extremely fast. Ths reveals that by settng the parameters to approprate values, the method s more effcent and sutable than the conventonal methods as an automated system for crmnal dentfcaton based on fngerprnts found at the crme scene.also, the Eucldan dstance and ts mean play a vtal role n dentfyng any gven nput mage wth ts correspondng stored template mages. References [1] S. Pankant, R.M. Bolle, A. Jan, Bometrcs: the future of dentfcaton, IEEE Comput. 33 (2) (2) [2] A. Jan, R. Bolle, S. Pankant (Eds.), Bometrcs: Personal Identfcaton n Networked Socety, Kluwer Academc, Dordrecht, [3] D. Mao and D. Malton, Drect Gray-Scale Mnutae Detecton n Fngerprnts, IEEE Trans. PAMI, Vol 19, No 1, pp 27-4, [4] G. T. Candela, P. J. Grother, C. I. Watson, R. A. Wlknson, and C. L. Wlson, PCASYS: A Pattern-Level Classfcaton Automaton System for Fngerprnts, NIST Tech. Report NISTIR 5647,August [5] H. C. Lee, and R. E. Gaensslen, Advances n Fngerprnt Technology, Elsever, New York, [6] S. Prabhakar, and A. K. Jan, Fngerprnt Classfcaton and Matchng, A PhD thess Submtted to Mchgan State Unversty, 21. [7] N. Ratha, K. Karu, S. Chen, and A. K. Jan, A Real-Tme Matchng System for Large Fngerprnt Databases, IEEE Trans. Pattern Anal.and Machne Intell., Vol. 18, No. 8, pp , [8] A. K. Jan, L. Hong, S. Pankant, and Ruud Bolle, An Identty Authentcaton System Usng Fngerprnts, Proceedngs of the IEEE, Vol. 85, No. 9, pp , [9] X. Jang, W. Y. Yau, Fngerprnt Mnutae Matchng based on the Local and Global Structures, Proc. 15th Internatonal Confererence on Pattern Recognton, Vol. 2, pp , Barcelona, Span, September 2. [1] A. K. Jan, L. Hong, and R. Bolle, On-lne Fngerprnt Verfcaton, IEEE Trans. Pattern Anal. and Machne Intell., Vol. 19, No. 4, pp , [11] A. K. Jan, S. Prabhakar, and L. Hong, A Multchannel Approach to Fngerprnt Classfcaton, IEEE Trans. Pattern Anal.and Machne Intell., Vol. 21, No. 4, pp , [12] A. K. Jan, S Prabhakar, L Hong, and S Pankant, Flterbank-based Fngerprnt Matchng, IEEE Trans. Image Proc.Vol 9, No 5, pp , 2. Volume 2, Issue 11, November 213 Page 267

10 Volume 2, Issue 11, November 213 ISSN [13] A. K. Jan, A. Ross, and S. Prabhakar, Fngerprnt Matchng Usng Mnutae and Texture Features, n proc. of the Int. Conf. on Image Processng (ICIP), Greece, pp , Oct. 21. [14] A. Ross, A. K. Jan, and J. Resman, A hybrd Fngerprnt Matcher, n proc. of the Int.Conf. on Pattern Recognton (ICPR), Quebec Cty, Aug.22. [15] A. K. Jan, S. Prabhakar, and S. Chen, Combnng Multple Matchers for a Hgh Securty Fngerprnt Verfcaton System, Pattern Recognton Letters, Vol 2, No , pp , Nov [16] A. K. Jan, L. Hong, S. Pankant, and R. Bolle, An Identty Authentcaton System usng Fngerprnts, Proc. of the IEEE, Vol. 85, No 9. [17] Pradeep M. Patl, Shekhar R Suralkar, and Fayaz B Shakh, System authentcaton usng hybrd features of fngerprnt, ICGST Internatonal Journal on Graphcs, Vson and Image Processng (GVIP), Issue 1, Vol. 6, July 26, pp Author: M.P. Deshmukh :- He receved M.E. from MNREC, Allahabad and presently persung hs PhD from North Maharashtra Unversty, Jalgaon (M.S.). He has 24 years of teachngexperence Prof (Dr) P.M. Patl:-l He s havng 25 years of experence and at present Drector & Prncpal RMD, SIT, Warje, Pune. He has several publcatons n natonal & Internatonal journals and number of research students are persung PhD under hs gudance. Volume 2, Issue 11, November 213 Page 268