Fingerprint Matching Using Invariant Moment FingerCode and Learning Vector Quantization Neural Network

A novel method for fingerprint matching using invariant moment fingerCode and 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 based on the reference point in the enhanced fingerprint image. Then a 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