Hierarchical palmprint feature extraction and recognition based on multi‐wavelets and complex network

Abstract

This study presents a hierarchical palmprint feature extraction and recognition approach based on multi-wavelet and complex network (CN) since they can effectively decrease redundant information and enhance key points of main lines and wrinkles. The palmprint is first pre-filtered and decomposed once using multi-wavelet. Three components (LL 1,2,3 ) corresponding to the pre-filter except for diagonal component are extracted as the elementary features. Second, binary images (BLL 1,2,3 ) are obtained by the average window method using different thresholds. Third, three series of dynamic evolution CN models (the 1st, 2nd, 3rd CNs) are constructed from global to local, which is based on the mosaiced images obtained from BLL 1,2,3 , BLL 1 and four equally divided sub-images of BLL 1 , respectively. Fourth, statistical features are extracted from complex networks, in which average degree and standard deviation of the degrees are extracted for the 1st CNs and average degrees are extracted for the 2nd and 3rd CNs. Fifth, the fisher feature is extracted using the linear discriminate analysis method. Finally, the nearest neighbourhood classifier is used to recognise palmprint. Based on the CASIA Palmprint Image Database, experimental results show that the proposed method can effectively recognise palmprint with good robustness and overcome the problem of small training samples number.