Pseudo-outer product based fuzzy neural network fingerprint verification system.

Abstract

Fingerprint identification has been used in law enforcement applications over the last century, and has become the de facto international standard for positive identification. With the emergence of automated fingerprint identification technologies, it has assisted in making the once labour-intensive process of classifying, searching and matching a thing of the past. As a biometrics proof of identification, not many have ventured into the world of fingerprint identification using fuzzy neural networks. In this paper, a database of fingerprint images is constructed and a fuzzy neural network called the pseudo outer product fuzzy neural network (POPFNN) [Zhou, R.W. & Quek, C. (1996). A pseudo outer-product based fuzzy neural network. Neural Networks, 9(9), 1569-1581] is trained to detect similarity between two fingerprints and decide whether they belong to the same person. The fundamental idea is that, given a person's fingerprints taken under different conditions, the POPFNN based fingerprint verification system should be sufficiently robust to distinguish the difference. The people providing the fingerprint samples are subjected to different 'adverse' conditions; from wetness to chemical treatments. Fingerprint images are taken after conditions such as: after a shower, holding pineapples (mild acid from fruit), after washing one's hands, etc. The characteristics of POPFNN, such as the learning, generalisation, and high computational abilities, make fingerprint verification particularly powerful when verifying authentic fingerprints subjected to external conditions and recognising spurious ones. In order to demonstrate the efficacy of POPFNN and its application in the fingerprint verification system (FVS), several types of experiments have been designed and implemented in this work. The experimental results and analysis are presented at the end of the paper for discussion.