Noisy and incomplete fingerprint classification using local ridge distribution models

Fingerprint images acquired from live-scan devices may have various noises, such as cuts and smears and be incomplete due to shifted and partial scanning. We propose a novel fingerprint classification method that is able to effectively classify noisy and incomplete fingerprints, which are acquired by live-scan devices. Fingerprint images are divided into blocks of 16×16 pixels and representative directional values of each block are extracted. Based on the representative directional values, the core blocks including the core points are identified by core block Markov models. Then, fingerprints are divided into 4 regions with respect to the core blocks and each region is modeled with the distribution of the ridge directional values in its region. Fingerprint classification is carried out by using the regional local models. If a fingerprint is given, each local model determines the probabilities that the given fingerprint belongs to all the fingerprint classes. The final decision on the classification is made by probabilistic integration of the classification results of local models. Since the proposed method analyzes ridges based on blocks of 16×16 pixels and classifies based on regional local models, it can be robustly applied to noisy and incomplete fingerprint images. A performance evaluation based on the live scanned fingerprint databases FVC 2000, 2002, and 2004 shows a good classification accuracy of 97.4%. A regional local model based fingerprint classification method is proposed.We make regional local models from the probability distributions of ridge directions.A classification accuracy based on the live scanned fingerprint databases is 97.4%.The classification performance is high for low quality and incomplete fingerprints.

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