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Comparative study of the different feature extraction algorithms used for fingerprint identification

21 March 2013


Nabanita Basu

The research work is particularly aimed at quantitative comparison of the various feature extraction techniques that could be used individually or in combination for fingerprint identification. To test the robustness of the feature extraction algorithms, I intend to use a dataset having fingerprints of varying quality and orientation.  The different feature extraction techniques that I intend to compare include, fingerprint minutiae extraction algorithm, principal component analysis and 2D Log Gabor filters. The quality of the fingerprint image greatly influences the performance of the feature extraction algorithms.  This research work aims at combining the best image processing techniques, feature extraction procedures and pattern matching algorithms that have been used in face recognition and other forensic investigation genres, to mark out a set of feature extraction techniques that could facilitate near accurate identification of low quality, crime scene fingerprints. The research work though concentrates on working with latent fingerprints, is extendable to impressed as also plastic/ visible fingerprints. This work would sure help revolutionize the entire process of fingerprint identification. The 2004 Brandon Mayfield case highlights the need for a better, more robust, statistically more accurate and hence more reliable system for pattern recognition in the forensic domain. This research work aims at addressing this particular need.