Publication

Abstract : Researchers have recently found that the finger knuckle print is an emerging and promising biometric identifier. In order to get better performance in a biometric system requires fusion of multiple instances within a modality. This paper makes use of fractional theory to propose an efficient feature level of fusion scheme named as a Fractional Cuckoo Search algorithm (FCS) for finger knuckle images. In this work both left index and right index features of an individual are extracted by using HOG method. These feature vectors are fused by optimal weight score selection, which finds out the weights from the proposed fractional cuckoo search algorithm. Thus the performance of the proposed system has been evaluated with the help of multi- layered SVM classifier in terms of False Acceptance Ratio (FAR), False Rejection Ratio (FRR) and Accuracy. From the experimental results, our proposed optimization system obtains a better accuracy of 98.97%.