On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint classification: a review
Pattern Analysis & Applications
Pores and Ridges: Fingerprint Matching Using Level 3 Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Application of Dimensionality Reduction Analysis to Fingerprint Recognition
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
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Law enforcement, border security and forensic applications are some of the areas where fingerprint classification plays an important role. A new technique based on wave atoms decomposition and bidirectional two-dimensional principal component analysis (B2DPCA) using extreme learning machine (ELM) for fast and accurate fingerprint image classification is proposed. The foremost contribution of this paper is application of two dimensional wave atoms decomposition on original fingerprint images to obtain sparse and efficient coefficients. Secondly, distinctive feature sets are extracted through dimensionality reduction using B2DPCA. ELM eliminates limitations of classical training paradigm; trains data at a considerably faster speed due to its simplified structure and efficient processing. Our algorithm combines optimization of B2DPCA and the speed of ELM to obtain a superior and efficient algorithm for fingerprint classification. Experimental results on twelve distinct fingerprint datasets validate the superiority of our proposed method.