Applying Learning Vector Quantization Neural Network for Fingerprint Matching

Abstract

A novel method for fingerprint matching using Learning Vector Quantization (LVQ) Neural Network (NN) is proposed. A fingerprint image is preprocessed to remove the background and to enhance the image by eliminating the LL4 sub-band component of a hierarchical Discrete Wavelet Transform (DWT). Seven invariant moment features, called as a fingerCode, are extracted from only a certain region of interest (ROI) of the enhanced fingerprint. Then an LVQ NN is trained with the feature vectors for matching. Experimental results show the proposed method has better performance with faster speed and higher accuracy comparing to the Gabor feature-based fingerCode method.