Biblio

Biometrics has enormous role to authenticate or substantiate an individual's on the basis of their physiological or behavioral attributes for pattern recognition system. Multimodal biometric systems cover up the limitations of single/ uni-biometric system. In this work, the multimodal biometric system is proposed; iris and hand geometry features are fused at feature level. The iris features are extracted by using moments and morphological operations are used to extract the features of hand geometry. The Chaos-based encryption is applied in order to enhance the high security on the database. Accuracy is predicted by performing the matching process. The experimental result shows that the overall performance of multimodal system has increased with accuracy, Genuine Acceptance Rate (GAR) and reduces with False Acceptance Rate (FAR) and False Rejection Rate (FRR) by using chaos with iris and hand geometry biometrics.

This paper proposes an enhanced method for personal authentication based on finger Knuckle Print using Kekre's wavelet transform (KWT). Finger-knuckle-print (FKP) is the inherent skin patterns of the outer surface around the phalangeal joint of one's finger. It is highly discriminable and unique which makes it an emerging promising biometric identifier. Kekre's wavelet transform is constructed from Kekre's transform. The proposed system is evaluated on prepared FKP database that involves all categories of FKP. The total database of 500 samples of FKP. This paper focuses the different image enhancement techniques for the pre-processing of the captured images. The proposed algorithm is examined on 350 training and 150 testing samples of database and shows that the quality of database and pre-processing techniques plays important role to recognize the individual. The experimental result calculate the performance parameters like false acceptance rate (FAR), false rejection rate (FRR), True Acceptance rate (TAR), True rejection rate (TRR). The tested result demonstrated the improvement in EER (Error Equal Rate) which is very much important for authentication. The experimental result using Kekre's algorithm along with image enhancement shows that the finger knuckle recognition rate is better than the conventional method.