Gabor Filter and Texture based Features for Palmprint Recognition

Abstract

In this paper, we propose an efficient personal identification system based on palmprint recognition. Palmprint is widely used in biometric-based identification system. Palmprint is robust and obtained in a simple way. After extracting region of interest (ROI), the ROI is passed through Gabor filters with different wavelengths and orientations. Then, binarized statistical image features (BSIF) of phase of outputs of Gabor filters are obtained. Different BSIF codes are combined together and then, the histogram of final BSIF code is calculated. Efficient features from histogram are calculated and are given to the K-nearest neighbor (KNN) classifier to perform personal identification. Experimental results on PolyU database demonstrate that proposed algorithm achieves the higher accuracy than the recently proposed algorithms.