Fingerprint Classification Using Nonlinear Discriminant Analysis
Cheonghee Park, Haesun Park · University of Minnesota Digital Conservancy (University of Minnesota) · 2003
We present a new approach for fingerprint classification based on nonlinear feature extraction. Utilizing the Discrete Fourier Transform, we construct reliable and efficient directional images to contain the representative part of local ridge orientations in fingerprints and apply kernel discriminant analysis to the constructed directionalimages, reducing the dimension dramatically and extracting most discriminant features. Kernel Discriminant Analysis is a nonlinear extension of Linear Discriminant Analysis (LDA) based on kernel functions. It performs LDA in the feature space transformed by a kernel-based nonlinear mapping,extracting optimal features to maximize class separability in the reduced dimensional space. We show the effectiveness of the feature extraction method in fingerprint classification. Experimental results show the proposed method demonstrates competitive performance compared with other published results.