Falcon-ART is a fuzzy neural network that can be used as fuzzy controllers or applied to areas such as pattern recognition, forgery detection and data analysis. Our previously proposed Falcon-DIC has stronger noise tolerance capability compared to the original Falcon-ART by employing a new clustering technique called Discrete Incremental Clustering (DIC). In this paper, Falcon-DIC is applied to perform direct gray-scale minutiae extraction. Fingerprint features extraction, or minutiae extraction, is an essential part of fingerprint identification systems. Most existing minutiae extraction methods require image pre-processing, such as binarization and thinning. Since these image processing techniques results in the loss of valuable information, our proposed approach can extract minutia directly from gray-scale fingerprint images. Experimental results show that Falcon-DIC based minutiae extraction has invariant ability to rotation and good performance on true acceptance.
['Guang Yang', 'Daming Shi', 'Chai Quek']
Fingerprint minutia recognition with fuzzy neural network