Integrated biometric verification system using soft computing approach - Abdul Rahman, Abdul Wahab; Ng, G. S.; Jonathan, A. (Kluwer Academic Publishers-Plenum Publishers, 2007)
Among various biometric verification systems, fingerprint verification is one of the most reliable and widely accepted. One essential part of fingerprint verification is the minutiae extraction system. Most existing minutiae extraction methods require image preprocessing or post processing resulting in additional complex computation and time. Hence, direct gray-scale minutiae extraction approach on the image is preferred. One of these approaches is the use of Fuzzy Neural Network (FNN) as a recognition system to detect the presence of minutiae pattern. Currently, the development of FNN as a tool of recognition has shown a promising prospect. Some researchers have proposed several types of FNN. In particular, a Generic Self Organizing Fuzzy Neural Network (GENSOFNN) has been shown to excel in comparison with other FNN. Therefore, a new approach to perform direct grayscale minutiae extraction based on GENSOFNN is proposed in this paper. Experimental results show the potential of using GENSOFNN for real-time point of sale (POS) terminal for verification.