A fingerprint segmentation method using a recurrent neural network

In this paper, we propose a segmentation method for identifying a fingerprint image with the variation of vertical length using a recurrent neural network (RNN). Group delay spectra and histograms of horizontal pixel line are used as input features fed into the RNN and two target output patterns with and without consideration of state dependency are introduced for learning. The method composed of the histogram learning and the state-dependent target indicates the best performance. When the tolerable segmentation error is 60 pixels, a segmentation rate of 97.2% is obtained. In comparison with the rule-based method, this method has an advantage of about 10%. Furthermore, we show that this method has a characteristic different from the rule-based method in regard to segmentation faults, and the learning with the state-dependent target is more effective than that without the dependency.