There is a need to have automated, secure and accurate human access control mechanisms for reliable identification of people in several social applications such as law enforcement, secure banking, immigration control, etc. The best mode in which the identity management can be realized is the biometric based authentication system which uses physiological (fingerprint [9] and [37], face [41], [40], [26] and [27], iris [11] and [29], etc.) or behavioral (signature, gait, etc.) characteristics. Biometrics based solutions are better than the traditional token or knowledge based identification systems as they are harder to spoof, easier to use and never be lost. In past few years, society have noticed great attention in hand based biometric recognition systems (e.g. palm print [5], fingerprint [9] and finger knuckleprint [47], [28] and [30]) because of their low cost acquisition sensors, high performance, higher user acceptance and lesser need of user cooperation. The pattern formations at finger knuckle bending [47] as well as palmprint region [5] are supposed to be stable (as shown in Fig. 1) and hence can be considered as discriminative biometric traits.
over all created chimeric multimodal datasets.
There is a need to have automated, secure and accurate human access control mechanisms for reliable identification of people in several social applications such as law enforcement, secure banking, immigration control, etc. The best mode in which the identity management can be realized is the biometric based authentication system which uses physiological (fingerprint [9] and [37], face [41], [40], [26] and [27], iris [11] and [29], etc.) or behavioral (signature, gait, etc.) characteristics. Biometrics based solutions are better than the traditional token or knowledge based identification systems as they are harder to spoof, easier to use and never be lost. In past few years, society have noticed great attention in hand based biometric recognition systems (e.g. palm print [5], fingerprint [9] and finger knuckleprint [47], [28] and [30]) because of their low cost acquisition sensors, high performance, higher user acceptance and lesser need of user cooperation. The pattern formations at finger knuckle bending [47] as well as palmprint region [5] are supposed to be stable (as shown in Fig. 1) and hence can be considered as discriminative biometric traits.over all created chimeric multimodal datasets.
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