Enhanced fingerprint identification using dynamic neural network and fringe-adjusted joint transform correlation

The parallel processing capability and adaptive filtering features of dynamic neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. The most important aspect of the fingerprint enhancement is the extraction of relevant details with respect to distributed complex features. For this purpose, an efficient dynamic neural filtering technique has been proposed in this paper. After the enhancement process, fingerprint identification ishas been achieved using joint transform correlation (JTC) algorithm. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it in this study. The identification test results are presented to verify the effectiveness of the proposed enhancement and identification algorithms.