Bouridane A

Boussakta S

Kurugollu F

Laadjel M

Newcastle University E-Prints

DEGRADED PARTIAL PALMPRINT RECOGNITION FOR FORENSIC INVESTIGATIONSM.Laadjel∗, F.Kurugollu, A.BouridaneECIT, Queens University BelfastBelfast BT3 9DT, United KingdomS.BoussaktaSEECE,University of Newcastle Upon TyneNewcastle Upon Tyne NE1 7RU, United KingdomABSTRACTPalmprint trait is emerging as a new and practical biometricsolution and a few systems using full palmprints have beenproposed. However, partial palmprints left at scenes of crimehave not yet been exploited to recognize and find crime sus-pects. In this paper, a modified well known algorithm, re-ferred to as Modified Phase Only Correlation (MPOC) is pro-posed to match poor quality (degraded) partial palmprints leftby suspects at crime scenes against a full palmprint registeredin a template database. Instead of using the conventional peakas the matching score between two palmprint images, the ratioof the conventional peak to the highest peak in the outside-lobe correlation plane is employed. Experiments were con-ducted on a generated database of 800 partial palmprints hav-ing an area less than 25% of that of the full palmprint im-ages from which blurred and degraded images were createdby adding an intense synthetic Gaussian noise. The resultsshow that the proposed method yields high recognition per-formance thus demonstrating its ability to tackle the problemof identifying low quality partial palmprint images giving anEqual Error Rate (EER) less than 0.4%.Index Terms— Partial Palmprint, Phase Only Correla-tion, Identification1. INTRODUCTIONPalmprint biometrics deals with automatically recognizingindividuals based on their unique palms features, such asthe principal lines, wrinkles, ridges, minutiae points, singu-lar points and texture, etc. Extensive work has been carriedout on full to full palmprint matching using a low resolu-tion palmprint images of less than 150 dpi for commercialapplications such as access control [1][2][3] [4][5]. The ad-vantages of using low resolution images are usage of low costsensor and less memory space as well as low computationalcomplexity leading to real-time performance. On the otherhand, using high resolution images (500dpi) in this contexthas been received little attention [6][7]. However, to the bestof our knowledge only [7] has discussed the problem of usingpalmprint for forensic investigation and law enforcement toidentify crime suspects by stating that crime statistics showsthat 25% of crime scenes contain only partial palmprints[7]. One of the most challenging factors behind using par-tial palmprint images to automatically recognize a suspectdepends on the accuracy of the matching decision and thepoor quality of the recovered noisy partial palmprints left atthe crime scene. A partial to full palmprint matching sys-tem has been proposed in [7] and uses the concept of ScaleInvariant Feature Transform (SIFT) and minutiae features atthe matching process. However, the limitations of this sys-tem are (i) the minutiae extraction is very difficult in poorquality noisy images, (ii) the huge number of SIFT keypoints(around 15,000 in small partial palmprint) make the match-ing not practical and very complex, and (iii) poor qualitynoisy palmprint images were not used in the evaluation of thealgorithm.In this paper a modified POC based noisy partial to fullpalmprint recognition technique is proposed to address theproblem of matching noisy partial palmprint images. POCis an image matching technique using only the phase com-ponents of 2D Discrete Fourier Transforms (DFTs) of givenpartial palmprint images. This technique was successfully ap-plied to fingerprint and iris recognition [8][9]. The contribu-tion of this paper is to demonstrate that this matching tech-nique can be modified to be highly effective for noisy partialto full palmprint recognition.The remainder of this paper is organized as follows: Sec-tion 2 gives a brief fundamental theory of the POC techniquewhile section 3 and section 4 detail some properties and themodified POC. Experimental results are given in section 5.Finally, a conclusion is drawn in section 6.2. PHASE ONLY CORRELATION (POC): ANOVERVIEWIn the Fourier transform domain, the phase has been shownto be more informative than the spectrum [8][10]. This sec-tion introduces the fundamental theory of phase based imagematching using the Phase Only Correlation.Let us consider two palmprint images f1(x, y) andf2(x, y) with F1(u, v) = A1(u, v)ejθ1(u,v) and F2(u, v) =A2(u, v)ejθ2(u,v) are their corresponding Fourier transforms,respectively, where A1,2(u, v) and θ1,2(u, v) are the ampli-tude and phase components, respectively. The Phase Only1513978-1-4244-5654-3/09/$26.00©2009 Crown ICIP 2009Authorized licensed use limited to: Newcastle University. Downloaded on May 24,2010 at 12:29:25 UTC from IEEE Xplore.  Restrictions apply. Fig. 1. POC between identical full and partial palmprint im-agesFig. 2. POC between identical clean full and noisy partialpalmprint imagesCorrelation function rf1f2(x, y) of the two palmprint imagesis given as follows:rf1f2(x, y) = ϕ−1{ F1(u, v) · F∗2 (u, v)|F1(u, v) · F ∗2 (u, v)|} (1)= ϕ−1{ej(θ1(u,v)−θ2(u,v))} (2)where ϕ−1 denotes the inverse of Fourier transform andF ∗2 (u, v) is the complex conjugate of F2(u, v). If the twopalmprint images are similar, their POC function would pro-duce a distinct sharp peak in the origin. On the other hand,if the two palmprint images are not similar, the peak valuedrops significantly. Therefore, the amplitude of this peak canbe used as an efficient similarity measure for palmprint imagematching.3. TRANSLATION AND ILLUMINATIONINVARIANCE OF THE POC METHODThe most remarkable properties of the POC are (i) invarianceto image translation, (ii) invariance to illumination change,and (iii) immunity against additional noise.Fig. 3. POC between identical full and partial different palm-print imagesLet us consider f3(x, y) the shifted and scaled image off2(x, y) by (x0, y0) and β, respectively. Therefore f3(x, y)can be written in terms of f2(x, y) as follows:f3(x, y) = βf2(x − x0, y − y0) (3)By applying the Fourier transform to Equation (3) the fol-lowing can be obtained :F3(u, v) = βe−j2π(x0u−y0v)F2(u, v) (4)From Equation (5) and Equation (4), the POC functionbetween f1(x, y) and f3(x, y) can be written as:rf1f3(x, y) = ϕ−1{e−j2π(x0u−y0v)ej(θ1(u,v)−θ2(u,v))} (5)= rf1f2(x − x0, y − y0) (6)The above equation shows that the correlation peak isshifted by (x0, y0) and its value is invariant to translationand illumination changes. Fig.2 shows the noise immunityof the POC where the POC is calculated between a clean fullpalmprint image and a partial palmprint images corruptedwith Gaussian noise with zero mean and a standard deviationof 40. As it can be seen. although the value of the correlationpeak decreases it is still discriminative.4. PROPOSED MODIFIED PHASE ONLYCORRELATION (MPOC)When using POC method the correlation peak value decreaseswhen the noise energy increase relatively without changingits shape and location [8]. To enhance the discrimination ofthe correlation peak and hence improve its immunity againstnoise, an alternative but simple new peak measure is proposedin this work. The new peak value nrf1f2(x, y) is determinedas the ratio of the main peak inside-lobe of 11× 11 pixel sizeat the origin to the highest peak in the remaining Outside-lobecorrelation plane as follows:nrf1f2(x, y) =arg max(rf1f2(x, y))arg max(Outside-lobe)(7)1514Authorized licensed use limited to: Newcastle University. Downloaded on May 24,2010 at 12:29:25 UTC from IEEE Xplore.  Restrictions apply. The main idea behind using the highest peak of theOutside-lobe in equation (7) is to augment the reliabilityof the peak discrimination, since the Outside-lobe correlationplane maintains flat, approximately flat and spurious peaksin the three following different matching scenarios, respec-tively: (i) two identical palmprint images (clean partial andclean full ) (ii) two identical palmprint images (noisy partialand clean full) (iii) two different palmprint images (cleanpartial and clean full) palmprint are matched as can be seenin figures (1,2 and 3). Therefore, the use of equation (7) en-sures the maximization of the correlation peak in the genuinematching case (divide by small value) though it marginallyaffects it in the imposter matching case (divide by same valuerange).(a) (b)(c) (d)Fig. 4. Partial palmprint samples (a) Full Palmprint, (b)Partial palmprint, (c) Same partial palmprint corrupted withGaussian noise , (d) Same partial palmprint blurred horizon-tally5. EXPERIMENTAL RESULTSAll the experiments are carried out on generated database col-lected using a live scan scanner. 200 full palmprint have beencaptured from 100 persons. Each person has been asked toprovide two images of his right and left hand. The imageshave been taken without any control such as illumination vari-ation, position and noise. Originally, the collected imageswere of size (2500 × 2500). However, only the palm areawas considered in the processing. Consequently, the size ofall the images used in the following experiments was reducedto 1024 × 1024 with 500 dpi resolution. The partial palm-print images were generated from the full palmprint imagesby cropping its quarters. The majority of the quarters repre-sent less than 25% of the full palmprint area. Two test setshave been used in the evaluation of the proposed approach.The first set contains 800 quarters palmprint corrupted byGaussian noise of zero mean and standard deviation of 0.04 asshown in Fig4(c). The second set contains 800 quarters palm-print blurred horizontally and vertically with a motion of 22pixels length as shown in Fig4(d). Two different experimentshave been carried on each test set. A matching is consideredcorrect if the matched partial and full palmprint are found tooriginate from same individual, otherwise it is considered in-correct. A total of 160,000 (200 × 800) matchings have beenperformed on each test set. The number of comparisons hav-ing a correct matching is 800 and the remaining are incorrect.False Acceptance Rate (FAR), False Rejection Rate (FAR)and EER are used to measure the system performance.Figure(5.(a)) depicts the genuine and imposter similaritydistribution using the first test set, as can be seen, both dis-tributions are very well separated. The Receiver OperatingCharacteristics (ROC) curve is also depicted in figure (5.(b))which is the plot of the FAR and FRR against all possibleoperating points. The curve clearly shows that the proposedtechnique performs very well, giving an EER (FAR = FRR)of 0.38% which is clearly very attractive result. The same ex-periment was conducted on the second test set. Figure(6.(a))and Figure(6.(b)) depict the similarity distributions and theROC curve, respectively. An EER of 0.03% has been obtainedachieving very low error and high Genuine Acceptance Rate(GAR) (GAR = 1-FRR) of about 100%.The results fairly show the robustness of the proposedtechnique in tackling the problem faced by crime scene inves-tigators in order to match the left low quality partial palmprintat crime scene to a reference database.6. CONCLUSIONThis paper proposed an efficient partial to full palmprintrecognition which is not yet exploited by researchers. AMPOC has been used to match low quality partial palm-print images to the full once registered in reference database.The proposed method yields an outstanding performanceand shows high robustness against additive noise and imageblurring.7. REFERENCES[1] Wei Jia, De-Shuang Huang, and David Zhang, “Palm-print verification based on robust line orientation code,”Pattern Recogn., vol. 41, no. 5, pp. 1521–1530, 2008.[2] David Zhang, Wai-Kin Kong, Jane You, and Michael1515Authorized licensed use limited to: Newcastle University. Downloaded on May 24,2010 at 12:29:25 UTC from IEEE Xplore.  Restrictions apply. 0 5 10 1500.050.10.150.20.250.30.350.40.45DistancePercentage (%)GenuineImposter(a)0 5 10 150102030405060708090100Matching scoreFalse rate (%)FRRFAREER = 0.38(b)Fig. 5. (a) Genuine and Imposter distribution correspondingto the test set corrupted by Gaussian noise of zero mean andstandard deviation of 40, (b) Corresponding ROC curveWong, “Online palmprint identification,” IEEE Trans.Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1050, 2003.[3] Adams Kong, David Zhang, and Mohamed Kamel,“Palmprint identification using feature-level fusion,”Pattern Recogn., vol. 39, no. 3, pp. 478–487, 2006.[4] Xiangqian Wu, David Zhang, and Kuanquan Wang,“Fisherpalms based palmprint recognition,” PatternRecogn. Lett., vol. 24, no. 15, pp. 2829–2838, 2003.[5] Guangming Lu, David Zhang, and Kuanquan Wang,“Palmprint recognition using eigenpalms features,” Pat-tern Recogn. Lett., vol. 24, no. 9-10, pp. 1463–1467,2003.[6] Nicolae Duta, Anil K. Jain, and Kanti V. Mardia,“Matching of palmprints,” Pattern Recogn. Lett., vol.23, no. 4, pp. 477–485, 2002.0 1 2 3 4 5 6 7 800.050.10.150.20.250.30.35DistancePercentage (%)GenuineImposter(a)0 1 2 3 4 5 6 7 80102030405060708090100Matching scorePercentage (%)FRRFAREER = 0.03(b)Fig. 6. (a) Genuine and Imposter distribution correspondingto the blurred test set, (b) Corresponding ROC curve[7] Anil Jain and Meltem Demirkus, “On latent palmprintmatching,” Tech. Rep. 48824, Matching state Univer-sity, may 2008.[8] Koichi Ito, Hiroshi Nakajima, Koji Kobayashi, and Tat-suo Higuchi, “A fingerprint matching algorithm usingphase-only correlation,” IEICE Trans. Fundamentals.,vol. 87-A, no. 3, pp. 682–691, 2004.[9] Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki, KojiKobayashi, and Hiroshi Nakajima, “An effective ap-proach for iris recognition using phase-based imagematching,” IEEE Trans. Pattern Anal. Mach. Intell., vol.30, no. 10, pp. 1741–1756, 2008.[10] Xuelei (Sherry) Ni and Xiaoming Huo, “Statistical in-terpretation of the importance of phase information insignal and image reconstruction,” Statistics & Probabil-ity Letters, vol. 77, no. 4, pp. 447–454, February 2007.1516Authorized licensed use limited to: Newcastle University. Downloaded on May 24,2010 at 12:29:25 UTC from IEEE Xplore.  Restrictions apply. 

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Degraded partial palmprint recognition for forensic investigations

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Degraded partial palmprint recognition for forensic investigations

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