Abstract

Fingerprint classification based on statistical and structural (RNN and SVM) approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology), our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN –extracted futures of segmentation algorithm which has used very sophisticated “region growing process”.