Application of Wave Atoms Decomposition and Extreme Learning Machine for Fingerprint Classification

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.