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

摘要: 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 bidirectional two-dimensional principal component analysis (B2DPCA) using extreme learning machine (ELM) for fast accurate image is proposed. The foremost contribution this paper application two dimensional original images to obtain sparse efficient coefficients. Secondly, distinctive feature sets extracted through dimensionality reduction B2DPCA. ELM eliminates limitations classical training paradigm; trains data at a considerably faster speed due its simplified structure processing. Our algorithm combines optimization B2DPCA superior classification. Experimental results twelve distinct datasets validate superiority our proposed method.