Multimodal Biometric Authentication Parameters on Humanbody

An essential feature of Multimodal Authentication Systems of biometric parameters in human body is categorized into two major set. These two set of biometric factors are analyzed in many effective research work . Also, a first and most important issue is that different applications and services employ each its own authentication method and use different credentials. In majority of application, the authentication is base on the principle of checking “What the user Knows”. Face and hand are two supersets which is used for authentication in our human body. Such like supersets are guaranteed by limiting in time complexity and space validity. Biometric system relies on person‟s behavioral and/or physiological characteristics as an alternative means of person authentication (traditional means being password, smart card, ID etc.). However, biometric system based solely on a single biometric may not always meet security requirements. Thus various number of biometric systems are emerging as a trend which helps in overcoming limitations of single biometric solutions, such as when a user does not have a quality sample to present to the system and reduces the ability of the system to be tricked fraudulently. A reliable and successful biometric system needs an effective fusion scheme to integrate the information presented by multiple matchers. In this research, we integrate results of different mono modal biometric matchers (face, ear ,iris,hand and foot ) with the logistic regression approach of rank level fusion method. In this approach, not only the outcomes of the these mono-modal matchers are considered, but also their effectiveness, based on previous research, are also considered for final rank aggregation. Here we are going to prove that an encapsulating system can provide better performance for the day today customer needs. Key WordsMultimodal Biometric system, Fusion, Logistic regression, Biometric identification , Biometric authentication . 1.Introduction: An essential feature of new applications services employ each its own authentication method and use different credentials. In the majority of applications the authentication is based on the principle of checking “What the user Knows”, the user simply use pins, usernames and password which are difficult to remember. If they are so simple that can be kept in mind. It‟s a given that biometric technologies can be combined to provide enhanced security. This combined use of two or more biometric technologies in one application is called a multimodal biometric system. A multimodal system allows for an even greater level of assurance of a proper match in verification and identification systems. Multimodal systems help overcome limitations of single biometric solutions, such as when a user does not have a quality sample to present to the system and reduce the ability of the system to be tricked fraudulently [2]. Various biometric systems have been developed for governmental and commercial applications. Most of these systems can verify, 1-to-1 match or identify a person in a small database, 1-to-many match. Real time large-scale identification is still a challenging problem in terms of matching speed and accuracy. Of existing biometric technologies Iris Code developed 1993 and continuously improved by Daugman [1]–[3] is able to identify a person in an extremely large database in real time. In the last few years authentication has become of paramount which importance both on corporate internet and on the global web. The assess right of a person has been faced with two different approaches. Biometric identification and biometric authentication. Biometric identification and biometric authentication are differentiated as follows :biometric identification occurs when an individual provides a sample biometric which sometimes without any additional knowledge the system must compare that sample with every stored (5) N.Suresh Singh, Dr.G. Suganthi, D.MohanaDhas / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera. com Vol. 2, Issue 3, May-Jun 2012, pp. 331-337 332 | P a g e record to identify a match. This is known as a oneto-many match ,and executed without any corroborating data. By contract ,biometric authentication occurs when an individual present a biometric sample and some additional identifying data, such as a photograph or password, which then compared with the sample for that individual. biometric authentication provides some inherent advantages as compared to other non-biometric identifiers . since biometric correspond to a direct evidence of personal identity versus possession of secrets which can be potentially stolen . Moreover, most of the times the biometric enrollment is executed in-person an in controlled environments making it very reliable for future use. Both engines try to check the user‟s credentials before granting access to computer system. In some issues related to strong biometric authentication methods are still unsolved. In this paper we analyze the potentiality of multimodal authentication for the user based on two super set of parameters of human body . Face and hand has a set of biometric parameter which is used for authentication. Biometric method is recognizing a person based on a physiological or behavioral characteristic. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. Examples of physiological characteristics include handor finger images, facial characteristics, ear shapes, iris or retina characteristics etc. Behavioral characteristics are traits that are learned or acquired. Signatures, voice, keystroke, gait pattern etc. are example of behavioral characteristics. Multiple biometrics could also involve multiple instances of a single biometric, such as, two fingerprints, two hands or two eyes.A reliable and successful multibiometric system needs an effective fusion scheme to combine the information presented by multiple matchers. The goal of fusion is to determine the best set of experts in a given problem domain and devise an appropriate function that can optimally combine the decisions rendered by the individual experts [4]. Evidences in a multibiometric system can be integrated in several different levels, such as, sensor level, feature level, match score level, rank level and decision level. Among all of the above fusion approaches, fusion at the sensor, match score, feature and decision levels have been extensively studied in the literature. Fusion at the rank level, however, is a new and significantly understudied problem [5], which has a high potential for efficient consolidation of multiple matcher‟s outputs. Many multimodal biometric systems with various methods of fusion and strategies have been proposed over the last decade to achieve higher accuracy performance from the multibiometric systems. However according to our literature review, very few of these research concentrated on rank level fusion methods for combining multiple biometrics. Our aim in this paper is to combining different biometric matchers (iris, ear, face, hand and foot) using rank level fusion to increase the performance and reliability of a human authentication system. 2. Research motivation 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 a 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 VQbased voice classifier and an optical-flow-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) userindependent and user-dependent, at the score level [8]. The LSB method is used in my paper[4] the security method usimg 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 variation-based iris verification system, with fusion methods at the matching score level by using unweighted and weighted sum rules.Aiming at the same issue, i.e., to reduce false acceptance and false rejection error rates, we fill the niche and develop a multibiometric system incorporating three unimodal experts for face, ear and iris. As, our main issue in this work is the fusion method, so, we use three established matching approaches for the three biometric traits. We can use neural network for face, eigenimage for ear and Hamming distance for iris. The multi monomodal matches are discussed below, N.Suresh Singh, Dr.G. Suganthi, D.MohanaDhas / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera. com Vol. 2, Issue 3, May-Jun 2012, pp. 331-337 333 | P a g e A. Hand-geometry-based systems The human hand provides the source for a number of physiological biometric features; the most frequently use the fingerprint, the palm print, the geometry of the hand and the geometry of fingers[9].