This paper presents a fast fingerprint verification algorithm that uses level-2 minutiae and level-3 pore and ridge features.
The proposed algorithm uses a two-stage process to register fingerprint images. In the first stage, Taylor series-based image transformation is used to perform coarse registration, while in the second stage, thin plate spline transformation is used for fine registration. A fast feature extraction algorithm is proposed using the Mumford–Shah functional curve evolution to efficiently segment contours and extract the intricate level-3 pore and ridge features. Further, Delaunay triangulation based fusion algorithm is proposed to combine level-2 and level-3 information that provides structural stability and robustness to small changes caused due to extraneous noise or non-linear deformation during image capture. The authors define eight quantitative measures, using level-2 and level-3 topological characteristics to form a feature supervector. A support vector machine performs the final classification of genuine or impostor cases using the feature supervectors. Experimental results and statistical evaluation show that the feature supervector yields discriminatory information and higher accuracy compared to existing recognition and fusion algorithms. (Published abstract provided).
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