A Fingerprint Identification System Using Adaptive FPGA-Based Enhanced Probabilistic Convergent Network

This paper explores the biometric identification and verification of human subjects via fingerprints utilising an adaptive FPGA-based weightless neural networks. The exploration espoused here is a hardware-based system motivated by the need for accurate and rapid response to identification of fingerprints which may be lacking in other alternative systems such as software based neural networks. The fingerprints are pre-processed and binarized, and the binarized fingerprints are partitioned into train- and test-set for the FPGA-based neural network. The neural network employed in this exploration is known as Enhanced Convergent Network (EPCN). The results obtained are compared to other alternative systems. They demonstrate the suitability of the FPGA-based EPCN for such tasks.