Abstract
Many algorithms have been developed for fingerprint identification. The main challenge in many of the applications remains in the identification of degraded images in which the fingerprints are smudged or incomplete. Fingerprints from the FVC2000 databases have been utilized in this project to develop and implement feature extraction and classification algorithms. Besides the degraded images in the database, artificially degraded images have also been used. In this paper we use features based on PCA (principal component analysis) and ICA (independent component analysis) to identify fingerprints. PCA and ICA reduce the dimensionality of the input image data. PCA- and ICA-based features do not contain redundancies in the data. Different multilayer neural network architectures have been implemented as classifiers. The performance of different features and networks is presented in this paper.
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