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Biometric systems identify individuals based on unique traits such as the face, fingerprints, iris etc. The main objective of the study is to understand the role of deep learning in the process of authentication as well as its application in the enhancement of security of biometric systems. We highlight the studies using deep learning approaches to authenticate enrolled users under ideal and non-ideal environmental conditions. We summarize these approaches and explore the challenges that continue to restrict the full potential of biometric systems. The foremost are: building robust algorithms for authentication, ensuring the security of enrolled templates and protecting systems against spoofing attacks. In this paper, we review the performance achieved by various studies in overcoming the aforesaid challenges, along with the potential improvements and future directions in this domain. [ABSTRACT FROM AUTHOR]

Weight initialization is the most important component which affects the performance of artificial neural network during training the network using Back-propagation algorithm. The initial starting weights have significant effect on the training. If the weights are too large then the sigmoid will saturate, that makes learning slow. If weights are too small then gradients are also too small. In this paper a new weight initialization method has been proposed. The results for the proposed weight initialization technique are compared against the random weight initialization method. In this paper the proposed weight initialization method is statistically analyzed. Ten different data sets out of which five sets of data are taken from UCI machine learning repository and five sets of data are generated using function approximation problems that are used. Resilient Back Propagation training algorithm is used for training the feed forward artificial neural network. The proposed weight initialization method gives better results when compared with random weight initialization technique. [ABSTRACT FROM AUTHOR]