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1. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 DATA LEVEL FUSION FOR MULTI BIOMETRIC SYSTEM USING FACE AND FINGER Shubhangi Sapkal Govt. College of Engineering, Aurangabad by multiple biometric sensors, algorithms, samples, units, orAbstract— In this work, most commonly used and accepted traits. In addition to improving recognition accuracy, thesebiometrics face and finger are used for data level fusion. systems are expected to improve population coverage, reduceMulti biometric systems are expected to improve population spoofing and be resilient to fault tolerance of different monocoverage, reduce spoofing and be resilient to fault tolerance modal biometric systems . Face recognition is aof different mono modal biometric systems. This system is nonintrusive method, and facial images are probably the mostdesigned for access control system requires more security common biometric characteristic used by humans to makesuch as allow to access important data, it is the false personal recognition. It is questionable whether the faceacceptance rate that is major concern in such applications. itself, without any contextual information, is a sufficientWe do not want to access the data even the risk of manually basis for recognizing a person from a large number ofexamining a large number of potential matches identified by identities with an extremely high level of confidence.the biometric system. Humans have used fingerprints for personal identification from many decades. But, fingerprints of a small fraction of the population may be unsuitable for the automatic Index Terms—Multi modal biometrics, Failure-to-enroll, identification because of genetic factors, aging,Fusion environmental, or occupational reasons (e.g., manual workers may have a large number of cuts and bruises on their fingerprints that keep changing)  . The initial idea and I. INTRODUCTION early work of this research have been published in part as Multimodal biometric systems are those which conference papers in , , .utilize, or have capability of utilizing, more than one The outline of the work is as follows. Section 2physiological or behavioral characteristic for enrollment, discusses approaches presented in the literature. Section 3verification, or identification. The reason for combining deals with image fusion. Section 4 extends to modes ofdifferent sensor modalities is to improve the recognition operations. Section 5 discusses on Wavelet Transform andaccuracy . Unimodal biometric systems have to contend Decomposition. Section 6 contains similarity Measures.with a variety of problems such as noisy data, intra-class Experimental results are given in section 7. Finallyvariations, restricted degrees of freedom, non-universality, conclusions are drawn in section 8.spoof attacks, and unacceptable error rates. Some of theselimitations can be addressed by deploying multimodalbiometric systems that integrate the evidence presented by II. RELATED RESEARCH ON MULTIMODAL BIOMETRICSmultiple sources of information . In , the data level fusion is used and the DWT For IDs application, multimodality may be an coefficients are selected as features and the image iseffective tool to reduce the Failure to Enroll (FTE) rate. The reconstructed with those features. Miguel Carrasco in sequential use of multiple modalities guarantees that the proposed a bimodal identification system that combines facenon-enrollable population is reduced drastically. and voice information. A probabilistic fusion scheme at theFurthermore, sequential use of modalities permits fair matching score level is used, which linearly weights thetreatment of persons that do not possess a certain biometric classification probabilities of each person-class from bothtrait . Here two inexpensive and widely accepted face and voice classifiers.biometric traits namely face and fingerprint is used. Human In , histogram equalization of biometric scoreface recognition has a tremendous potential in a wide variety distribution is successfully applied in a multimodal personof commercial and law enforcement applications. verification system composed by prosodic, speech spectrumConsiderable research efforts have been devoted to the face and face information. Furthermore, a new bi-Gaussianrecognition problem over the past decade. Although there are equalization (BGEQ) is introduced. Stephen J. Elliott in ,a number of face recognition algorithms which work well in outlines the perceptions of 391 individuals on issues relatingconstrained environments, face recognition is still an open to biometric technology. Results demonstratedand very challenging problem in real applications . overwhelming support for biometrics applications involvingBiometrics has long been known as a robust approach for law enforcement and obtaining passports, while applicationsperson authentication. However, most mono modal involving time and attendance tracking and access to publicbiometrics are proven to exhibit one or more weaknesses. schools ranked lowest on the list.Multi biometric systems combine the information presented A bimodal biometric verification system based on k-Nearest Neighbourhood (k-NN) classifiers in the decision fusion Manuscript received April 07, 2012. module for the face and speech experts is discussed in . Shubhangi Sapkal, Computer Science and Engineering In , a method of speaker recognition is introduced basedDepartment,Government College of Engineering, Aurangabad., India. 80 All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012on multimodal biometrics by using the kernel fisher biometric characteristics do not have to be acquireddiscriminant analysis. Michał Chora  proposed a system simultaneously. Further, a decision could be arrived aton the basis of ear, palm and lips images for human without acquiring all of the traits. This reduces the overallidentification. The combination of iris and fingerprint recognition time. In the hierarchical scheme, individualbiometrics is used in . Jian Yang  proposed an classifiers are combined in a treelike structure. This workunsupervised discriminant projection (UDP) technique for used parallel mode for fusion of face and finger.dimensionality reduction of high dimensional data in smallsample size cases. UDP can be seen as a linear approximation V. WAVELET TRANSFORMof a multimanifolds-based learning framework which takesinto account both the local and nonlocal quantities. The The biometrics image fusion extracts informationmethod is applied to face and palm biometrics and is from each source image and obtains the effectiveexamined using the Yale, FERET, and AR face image representation in the final fused image . Thedatabases. aim of image fusion technique is to process the M. K. Shahin, A. M. Badawi Proposed in  threebiometric modalities for validating and implementing fusing detailed information which obtains frommultimodal biometric system, that are hand vein, hand both the source images. The multi-resolutiongeometry and fingerprint. image used to represent the signals where decomposition is performed for obtaining finer detail. Multi-resolution image decomposition gives III. IMAGE FUSION an approximation image and three other imagesThe three possible levels of fusion are: fusion at the feature viz., horizontal, vertical and diagonal images ofextraction or data level , fusion at the matching scorelevel  , ,  ,  and fusion at the decision coarse detail. The face and fingerprint images arelevel , , . obtained from different sources. After re-scaling, the(a) Fusion at the data or feature level: Either the data itself or images are fused by using wavelet transform andthe feature sets originating from multiple sensors/sources are decomposition. Finally, we obtain a completelyfused . The data obtained from each sensor is used to new fused image, where both the attributes of facecompute a feature vector. As the features extracted from onebiometric trait are independent of those extracted from the and fingerprint images are focused and reflected.other, it is reasonable to concatenate the two vectors into a The proposed image fusion rule selects the largersingle new vector. The new feature vector now has a higher absolute values of the two wavelet coefficients atdimensionality and represents a person‘s identity in a each point. Therefore, a fused image is produceddifferent hyperspace. Feature reduction techniques may be by performing an inverse wavelet transform basedemployed to extract useful features from the larger set offeatures. on integration of wavelet coefficients correspond(b) Fusion at the matching score level: Each system provides to the decomposed face and fingerprint images. Morea matching score indicating the proximity of the feature formally, wavelet transform decomposes an imagevector with the template vector. These scores can be recursively into several frequency levels and eachcombined to assert the veracity of the claimed identity. level contains transform values.(3) Fusion at the decision level: Each sensor can capture Finally, inverse wavelet transformation ismultiple biometric data and the resulting feature vectorsindividually classified into the two classes––accept or reject. performed to restore the fused image. The fusedA majority vote scheme can be used to make the final image possesses good quality of relevantdecision . information for face and fingerprint images. IV. MODES OF OPERATION In this work daubechies2 (Fig. 2) wavelet family A multi biometric system can operate in one of three for decomposition (Fig. 1) is used.different modes: serial mode, parallel mode, or hierarchicalmode. In the serial mode of operation, the output of onebiometric trait is typically used to narrow down the numberof possible identities before the next trait is used. This servesas an indexing scheme in an identification system. Forexample, a multi biometric system using face and fingerprintscould first employ face information to retrieve the top fewmatches, and then use fingerprint information to convergeonto a single identity. This is in contrast to a parallel mode of Fig. 1: Wavelet decompositionoperation where information from multiple traits is usedsimultaneously to perform recognition. This difference iscrucial. In the cascade operational mode, the various 81 All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 Threshold FRR FAR 0.6 0.14 0 0.65 0.32 0 0.7 0.44 0 0.75 0.56 0 0.8 0.75 0 0.85 0.87 0Fig 2: A daubechies 2 wavelet Table 1: Recognition performance for different threshold values using ‗MEAN‘ fusion technique VI. EXPERIMENTAL EVALUATION Threshold FRR FARA typical biometric recognition system commits two 0.6 0.34 0.21types of errors: false acceptance and false 0.65 0.4 0.19rejection; a distinction has to be made between 0.7 0.43 0.07positive and negative recognition; in positive 0.75 0.61 0recognition systems (e.g., an access control 0.8 0.79 0 0.85 0.88 0system) a false match determines the false Table 2: Recognition performance for different thresholdacceptance of an impostor, whereas a false values using ‗MAX-MIN‘ fusion techniquenon-match causes the false rejection of a genuineuser. On the other hand, in a negative recognitionapplication (e.g., preventing users from obtainingwelfare benefits under false identities), a falsematch results in rejecting a genuine request,whereas a false non-match results in falselyaccepting an impostor attempt. The notation ―falsematch/false non-match‖ is not applicationdependent and therefore, in principle, is preferableto ―false acceptance/false rejection.‖ However, theuse of false acceptance rate (FAR) and false Graph 1: - FAR-FRR diagram for MEAN methodrejection rate (FRR) is more popular and largelyused in the commercial environment . Positiverecognition system is considered in this work andcorrelation is used as similarity measure.FRR is False Rejection Ratio, which means thefault when someone which registered in the systemwas refused by system . Table I presents theFRR values of genuine person faces.FAR is False Acceptance Rate, which is the faultwhere someone of user which does not enlist will Graph 2: - FAR-FRR diagram for MAX-MINbe held true by the system. FAR values for methodimpostor persons are presented in Table II.Finally, Table III presents the FAR and FRR VII. CONCLUSIONvalues for all persons with different thresholdvalues. The FRR and FAR for number of A 2D Discrete Wavelet Transform is proposed toparticipants (N) are calculated as specified in Eq. capture the characteristics in faces and(1) and in equation Eq. (2): fingerprints. Experimental results on an extensive 1N set of face (FETRET database) and fingerprint () FRR FRR N1 n n …(1) database (FVC-2004 database) demonstrate that the proposed correlation and wavelet method 1N outperforms in identification. It is shown that the () n…(2 FAR FAR ) N1 n proposed method gives satisfying results for threshold-0.7. There is a trade-off between FAR 82 All Rights Reserved © 2012 IJARCSEE
ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012and FRR values. In some access control system  Masatsugu Ichino, Hitoshi Sakano and Naohisawith more security such as allow to access some Komatsu , ―Multimodal Biometrics of Lip Movements and Voice using Kernel Fisher Discriminant Analysis ―,ICARCVimportant data, it is the false acceptance rate that is 2006 IEEEmajor concern that is, we do not want to access the  Michał Chora, ―Emerging Methods of Biometricsdata even the risk of manually examining a large Human Identification‖, 2007 IEEE.number of potential matches identified by the  Stelvio Cimato, Marco Gamassi, Vincenzo Piuri,biometric system. Result shows that FAR is 0, Roberto Sassi and Fabio Scotti, ―Privacy-aware Biometrics: Design and Implementation of a Multimodal Verificationwhich can be applied in such applications. This System‖, 2008 Annual Computer Security Applicationswork can be extended to feature level fusion to Conference, 2008 IEEE pp. 130-139.improve accuracy and robustness.  Jian Yang, David Zhang, Jing-yu Yang, and Ben Niu, ―Globally Maximizing, Locally Minimizing: UnsupervisedReferences Discriminant Projection with Applications to Face and Palm Satyanadh Gundimada and Vijayan K. Asari, “Facial Biometrics‖, IEEE Transactions on Pattern Analysis AndRecognition Using Multisensor Images Based on Localized Machine Intelligence, pp.650-664, 2007.Kernel Eigen Spaces‖, IEEE Transactions on Image  M. K. Shahin, A. M. Badawi, M. E. Rasmy, ―AProcessing, Vol. 18, No. 6, PP. 1314-1325, June 2009 Multimodal Hand Vein, Hand Geometry, And Fingerprint Arun Ross and Anil K. Jain, “Multimodal Biometrics: An Prototype Design For High Security Biometrics‖, CIBEC08,Overview‖, Proc. of 12th European Signal Processing 2008 IEEE.Conference (EUSIPCO), pp. 1221-1224, September 2004.  A. Rattani, D. R. Kisku, M. 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ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012International Journal of Computer Science and Application,Issue-I, pp. 62-65, 2010. Davide Maltoni, Dario Maio, Anil K. Jain, SalilPrabhakar, Handbook of FingerprintRecognition(Springer), pp 3. Neil Yager and Ted Dunstone, ―The BiometricMenagerie‖, IEEE Transactions On Pattern Analysis AndMachine Intelligence, VOL. 32, NO. 2, FEBRUARY 2010,220-230. Website: http://www.bromba.com/faq/biofaqe 84 All Rights Reserved © 2012 IJARCSEE