A Certificate of Identification Growth through Multimodal Biometric System

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1 A Certificate of Identification Growth through Multimodal Biometric System Abstract: Automatic person identification is an important task in our life. Traditional method of establishing a person s identity includes knowledge based like password or token base like id cards we provide a biometric authentication system which is more reliable as compared to the traditional security system and it will overcome all the limitations of traditional method. Unimodal biometric systems contend with a variety of problems such as noisy data, restricted degrees of freedom, non-universality and spoof attacks. Some of these limitations can be overcome by employing multi-modal biometric identification technologies. This paper proposes a technique for human identification using fusion and different approaches method for face, ear, iris and foot data Keywords: Unimodal, Multimodal, neural network, eigenimage, Hamming distance, Sequential modified Haar transform. 1. INTRODUCTION Identity management system is challenging task in providing authorized user with secure and easy access to information and services across a wide verity of networked system. With the rapid development of information technology, the traditional personal identification methods are unable to meet the higher demands in accuracy, safety and practicality Compare with the traditional identification method.biometrics is a kind of more accurate, reliable and practical way of personal identification Attempting to improve the performance of individual matchers in such situation may not prove to be effective because of these inherent problems. Multi-biometric systems seek to alleviate some of these drawbacks by providing multiple evidence of same identity. These system help achieve an increase in performance that may not be possible using single biometric indicator.further, Multi-biometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously. However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts. 2. INFORMATION FUSIONIN BIOMETRICS In recent years, multimodal biometrics has become an important research trend in improving biometric accuracy [2]-[6]. For example, multiple biometric traits can be captured by different sensors; a number of biometric procedures can be applied to provide a variety of Mrs.Snehlata Barde SSGI Bhilai (C.G.) information; different matching algorithms can be developed to yield independent match scores; and several rules can be used to make decisions on multimodal biometric outputs. The challenge is in combining separate biometric technologies systematically. To enable a decision based on multimodal biometrics to be superior to that based on a single bio-metric technology, information ftision technology has been introduced to integrate multiple biometrics to achieve better performance [2]- [4].Matcher Fig. 1. Possible levels of information fusion in a multimodal biometric System 3. MULTI MODAL BIOMETRIC SYSTEM Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a verification /identification system. Identification based on multiple biometrics represents an emerging trend. The most compelling reason to combine different modalities is to improve the recognition rate. This can be done when biometric features of different biometrics are statistically independent. There are other reasons to combine two or more biometrics. One is that different biometric modalities might be more appropriate for the different applications. Another reason is simply customer preference.the aim of multi-biometrics[2] is to reduce one or more of the following: False accept rate (FAR) False reject rate (FRR) Failure to enroll rate (FTE) Susceptibility to artifacts or mimics The accuracy of a multimodal biometric system is usually measured in terms of matching errors and image acquisition errors. Matching errors consist of false match rate (FMR) where an impostor is accepted and false non- Volume 2, Issue 2 March April 2013 Page 68

2 match rate (FNMR) where a genuine user is denied access. Image acquisition errors comprise of failure-toenrol (FTE) and failure-to-acquire (FTA). A summary of the different biometric errors is provided in Table I biometric system has five main modules: I. Sensor module II. Feature extraction module III. Matching module IV. Decision module V. System database module. The sensor module is responsible for acquiring the biometric data from and individual. The feature ory features to represent the underlying trait. The matching module compares extracted features against the stored templates to generate match scores. The decision module uses the match scores to either validate a claimed identity or determines the user s identity. The system database module acts as the repository of biometric information.. However, if the matching score of a particular trait is not bounded, we can estimate the minimum and maximum value from the training set of match scores of that trait. Let x and y denote the matching score before and after normalization, respectively.the Min-Max technique computes y as x min (S x ) y= max(s x ) min(s x ) where S x is the set of all possible matching scores generated by a particular trait. Min-Max normalization retains the original distribution of scores and transforms all the scores into a common range[0,1], 3.2 Fusion Technique The fusion technique used in the experiment is based on the different weight assignment to each biometric trait. These different weight are assigned on the basis of their Equal Error Rate (EER). For i th particular trait, weight W i is calculated as 1/EERi W i = (1/EERj) Where EERj is the equal error rate for j th trait and n represents the number of traits participating in fusion. Then the fused score S is computed as S= (W j S j ) Where S j is the match score of j th trait. the range of fused score S calculated by this technique is also[0,1] because W j =1 4. MULTIMODAL BIOMETRIC SYSTEMS ARCHITECTURES Once it has been determined which different biometric sources are to be integrated, the system architecture is selected. It is generally accepted that there are two main types of system designs[6] when it comes to multimodal biometric systems namely serial and parallel 3.1 Normalization Technique We used the Min-Max normalization technique.in this technique, we shift the minimum and maximum bounds of the scores produced by a particular trait to 0 and 1, respectively. Figure 2. Two Main Architecture Designs for Multimodal Systems Volume 2, Issue 2 March April 2013 Page 69

3 4.1 Serial In serial architecture[6], also known as cascade architecture, the processing of the different inputs are done in sequence.therefore, the output from the first biometric trait, will affect the processing of the 2nd biometric trait, and so forth. 4.2 Parallel In parallel architecture, the processing of different biometric inputs[6] are done independently from each other. Once both have been separately processed, their results are combined. 5. RELATED PREVIOUS WORK From the last decade, several approaches have been proposed and developed for multimodal Biometric authentication system. In 1998, a bimodal approach was proposed by L. Hong and A. K. Jain for a PCA-based face and minutiae-based fingerprint identification system with a fusion method at the decision level [6]. In 2000, R. Frischholz and U. Dieckmann developed a commercial multimodal approach BioID, for a model-based face classifier, a VQ-based voice classifier and an opticalflow-based lip movement classifier for verifying persons [7]. In 2003, J. Fierrez-Aguilar and J. Ortega-Garcia proposed a multimodal approach including a face verification system based on a global appearance representation scheme, a minutiae-based fingerprint verification system and an on-line signature verification system based on HMM modeling of temporal functions, with fusion methods, sum-rule and support vector machine (SVM) user-independent and user-dependent, at the score level[8].the LSB method is used in G. Suganthi and N. Suresh Singh.[9] the security method using the biometric parameter fingerprint. In the same year, Wang and others proposed a multimodal approach for a PCAbased face verification system and a key local variationbased iris verification system, with fusion methods at the matching score level by using un-weighted and weighted sum rules. Aiming at the same issue, i.e., to reduce false acceptance and false rejection error rates, Kong et al. [10] proposed a weighted image fusion of visible and thermal face images where weights are assigned empirically on the visible and thermal face images by decomposing them using wavelet transform. Bebis et al. [12] employed a Genetic Algorithm for feature selection and fusion where group of wavelet features from visible and thermal face images are selected and fused to form a single image. Here there is no scope for weighting. Singh et al. [11] proposed a weighted Image fusion using 2V-SVM where weights are assigned by finding the activity level of visible and thermal face image. 6. PROPOSOSED PLAN OF RESERCH WORK The main goal of research is to improve the recognition performance of a biometric system by incorporating multiple biometric traits. An effective fusion is the key of success multimodal biometric system which is necessary to combine the information presented by multiple domain experts. In this work, I will provide fusion at the rank level for consolidating the rank information produced by the three separate monomodal matchers. There are many ways for rank consolidation-such as, majority rule, positional methods, utilitarian methods, multi-stage method etc. In this work I will use the positional method, which considers the relative position of the element in the ranked list. Main issue in this work is the fusion method use three established matching approaches for the biometric traits. I can use neural network for face, eigenimage for ear and Hamming distance for iris, Sequential modified Haar transform for footprint etc. Face Matcher For the face matcher, we use nureal network approach (all parts of the face images are used for training and recognition purposes). One of the most widely used representations of the face region which uses this approach is Eagan face [11], which is based on principal component analysis. Ear matcher We initialize the ear matching process by acquiring the training set, i.e. the images of ear. Then we computer eigen vectors and eigenvalues on the covariance matrix of those images [11]. The M highest eigenvectors are kept. Finally, the known images are projected onto the image space, and their weights are stored. This process is repeated as necessary. Iris Matcher The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. Formation of the unique patterns of the iris is random and not related to any genetic factors [13]. The iris recognition system is composed of a number of subsystems, such as, segmentation locating the iris region in an eye image, normalization creating a dimensionally consistent representation of the iris region, feature encoding creating a template containing only the most discriminating features of the iris and matching final recognition of the test iris with the template. Footprint Recognition Footprint identification is the measurement of footprint features for recognizing the identity of a user[15]. Footprint is universal, easy to capture and does not change much across time. Footprint biometric system does not require specialized acquisition devices. Footprint image of a left leg is captured for hundred people in different angles. No special lighting is used in this setup. The foot image is positioned and cropped according to the key points. Sequential modified Haar transform is applied to the resized footprint image to obtain Modified Haar Energy (MHE) feature. The sequential modified Haar wavelet can map integer-valued signals onto integer-valued signals abandoning the property of perfect construction. The MHE feature is compared with the feature vectors stored in database using Euclidean istance. The accuracy of the MHE feature and Haar nergy feature under different ecomposition levels and combinations are compared. Volume 2, Issue 2 March April 2013 Page 70

4 fig 3 Block diagram of the proposed multibiometric system 7. CONCLUSION More investigations will be carry out in the domain of multibiometrics, investigation of good combination of multiple biometric traits and various fusion method will get the optimal identification results. Multimodal biometric system using face, ear, iris, hand, and foot and biometrics incorporating various rank level fusion methods. We will use the three positional methods of rank fusion approach, the logistic regression method, The Sequential Modified Haar Transform for the footprint and The LSB on ridges of fingerprint for recognition accuracy. We convert the matching score of all traits in the rang of 0 to 1 similarity score by Min-Max normalize techniques. The upper bound findout of matching scores for different trains is taken from its training data. Calculation of EER using FAR/FRR curve for all Biometric traints. And Accuracy using of genuine and imposter for both approches. As these considerations have significant influence on the effectiveness of various recognition approaches, using a true multimodal Database in real-time environment and incorporating dual or tri-level fusion approaches are promising future directions of research REFERENCES [l] R. Brunelli and D. Falavigna, Person identification using multiple cues, IEEE Transactions on PAMI, vol. 17, pp ,October [2] A..K. J. A. Ross and J. Z. Qian, Information fusion in biometrics, in Proc. 3rd International Conference on Audio- and Video-Based Biometric Person Authentication, Halmstad, Sweden, pp , June tml. [3] G. L. Marcialis and F. Roli, Fusion of LDA and PCA for face verification. Springer Verlag, [4] J. Kittler and E. F. Roli, Decision-level fusaon in fingerprint uerification. Springer Verlag, [5] C. Sanderson, Information fusion and person verification using speech and face information. September [6] R. W. Frischholz and U. Dieckmann, Bioid: A multimodal biometric identification system, IEEE Computer, vol. 33, pp , February [7] L. H. A. K. Jain and Y. Kulkarni, A multimodal biometric system using fingerprint, face, and speech, in Proc. 2nd International Conference on Audio- and Video- Based Biometric Person Authentication, Halmstad, Sweden, pp , March NUM= [8] John D. Woodward, Jr., Christopher Horn,Julius Gatune, and Aryn Thomas - Biometrics A Look at Facial Recognition - RAND Public Safety and Justice for the Virginia State Crime Commission ISBN: Published by RAND1700 Main Street, P.O. Box 2138, Santa Monica. [9]Arun Ross and Anil K. Jain MULTIMODAL BIOMETRICS: AN OVERVIEW, Appeared in Proc. of 12th European Signal Processing Conference (EUSIPCO), (Vienna, Austria), September pp [10]. Mayank Vatsa, Richa Singh, P. Mitra,Mayank Vatsa, Richa Singh, P. Mitra Digital Watermarking based Secure Multimodal Biometric System, IEEE lntemational Conference on Systems, [11] L. Hong, and A K. Jain, Integrating faces and fingerprints for personal identification, IEEE Trans. on PAMI, vol. 20, no. 12, pp ,1198. [12] R. Frischholz, and U. Dieckmann, BioID: A multimodal biometric identification system,, IEEE Computer, vol. 33,no.2, pp , [13] J.Fierrez-Aguilar et al., A comparative evaluation of fusion strategies for multimodal biometric verification, Proc. of the 4 th Int.Con. J. Kittler, and M. Nixon, Eds., pp ,2003. [14] G.Suganthi and N.Suresh Singh Ridge Enhanced steganography based on LSB and ArnoldTransformation,, ISSN: X, ijetca,oct2011. [15].J.Heo,S.Kong,B.Abidi,and M.Abidi, Fusion of visible and thermal signatures with eyeglass removal for robust face recognition, in IEEE workshop on Object Tracking and Classification Beyond the visible spectrum in conjunction with (CVPR- 2004), Washington, DC, USA, 2004, pp [16] R.Singh,M.Vatsa,and A.Noore, Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face Volume 2, Issue 2 March April 2013 Page 71

5 recognition, Pattern Recognition, vol. 41, pp , [17] V.Chatzis,A.G.Bors,and I.Pits, Multimodal decisionlevel fusion for person authentication, IEEE Transactions on systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 29, no. 6, pp , [18] S. Ben-Yacoub,Y.Abdeljaoued, and E.Mayoraz, Fusion of face and speech data for person identity verification, IEEE Transactions on Neural Networks, vol. 10,pp , [19]A.Ross and A.K.Jain, Information fusion in biometrics, Pattern Recognition Letters, vol. 24, no. 13, pp , [20] A.Ross and R.Govindarajan, Feature level fusion using hand and face biome trics, in Proceedings of SPIE Conference on Biometric Technology for Human Identification, 2004, pp [21] S.Prabhakar and A.K.Jain, Decision level fusion in fingerprint verification, Pattern Recognit ion, vol. 35, no. 4, Volume 2, Issue 2 March April 2013 Page 72