The Face Recognition Algorithms Based on Weighted LTP

Local Ternary Pattern (LTP) is usually applied for texture classification problems. LTP extends the Local Binary Pattern using the custom threshold and encoding the small pixel difference into third state. Since the amount of information in different face regions is not equal, this paper proposes an approach of weighted LTP to show facial feature effectively. First, the original face image is divided into small blocks, and the LTP characteristic value and histogram of each piece of pixel are calculated. Then the weight of sub histogram is calculated by information entropy and the histogram of whole face image cascade of the histogram of all sub regions, finally, the weighted histogram of whole face image similarity are calculated by chi-square distance, the classification is performed by a nearest neighbor classifier. Experimental results show a better performance on ORL and Yale face database.