A new model for fingerprint classification by ridge distribution sequences

In this paper, a new method is introduced which is a combination of structural and syntactic approaches for fingerprint classification. The goal of the proposed ridge distribution (R-D) model is to present the idea of the possibility for classifying a fingerprint into the complete seven classes in the Henry's classification. From our observation, there exist only 10 basic ridge patterns which construct fingerprints. Fingerprint classes can be interpreted as a combination of these 10 ridge patterns with different ridge distribution sequences. In this paper, the classification task is performed depending on the global distribution of the 10 basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. The regular expression for each class is formulated and a NFA model is constructed accordingly. An explicit rejection criterion is also defined in this paper. For the seven-class fingerprint classification problem, our method can achieve the classification accuracy of 93.4% with 5.1% rejection rate. For the five-class problem, the accuracy rate of 94.8% is achieved. Experimental results reveal the feasibility and validity of the proposed approach in fingerprint classification. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.