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An Effective Approach to Fingerprint Segmentation using Fisher Basis

Fingerprint image segmentation is an integral part of an automatic fingerprint recognition system. In this paper an effective algorithm for fingerprint image segmentation is presented. This technique is based on the fusion of multiple features for segmentation that are projected onto a one dimensional feature space using Fisher discriminant analysis. The classification of the fingerprint regions as foreground or background is carried out by the use of learning vector quantization (LVQ) neural networks. The proposed method exhibits low segmentation error (1.8%) and its high performance makes it an excellent candidate for use in online fingerprint verification systems