Palmprint recognition is a relatively new biometric recognition technology,extracting the optimal classifying features from palmprint always is an important research area in the palmprint recognitio n field.Palmprint images have rich texture features,but traditional methods are difficult to accurately characterize them.In order to solve this problem,a palmp rint recognition method based on hybrid Gabor filter and weighted center symmetr ic local binary pattern (WCS-LBP) is proposed by combining fixed scale and adap tive multi-scale Gabor filter.Firstly,using the hybrid Gabor filter to extract the region of interest of palmprint to obtain a feature image,and connect it in series in the palmprint feature space.Then,using the WCS-LBP to extract the spa tial palmprint features to form a feature vector.Finally,the classification is a chieved by matching the similarity of WCS-LBP histogram sequences.Experiments w ere carried out in PolyU library,Tongji University library,IIT-D library and se lf-built non-contact library.The results show that the highest recognition rat es obtained by this algorithm are 99.7685%,99.5109%,99.0916% and 98.5010%,and the l owest equal error rate rates are 0.7945%,1.2357%,1.6725% and 2.3391%,respectively,an d recognition time is within 1s,which is superior to other traditional and popul ar algorithms and shows good results.