Fingerprint classification based on subclass analysis using multiple templates of support vector machines

Fingerprint classification reduces the searching time of an automated fingerprint identification system. Since fingerprints have properties of intra-class diversities and inter-class similarities, the ambiguous example causes a difficult problem in the fingerprint classification. In order to address the problem, we have analyzed fingerprints' subclasses with multiple decision templates. It clusters the soft outputs of support vector machines (SVMs) into several sub-classes using the self-organizing maps, and estimates a localized template for each sub-class. For an input fingerprint, the proposed method matches the output vector of SVMs to each template and finally categorizes the sample into the class of the most similar template. Experimental results on the FingerCode dataset demonstrate the effectiveness of the subclass-based approach compared with previous methods.