Texture descriptors for better face recognition performance

Due to, the great electronic development, which reinforced the need to define people’s identities, different methods, and databases to identification people’s identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40,401 and 10 subjects taken from ORL, UFI and Self-Created databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database as standard database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance. Hereby, the LBP based biometric system performs excellence recognition accuracy even with difficult face images that contain different expressions, different poses, and varied illumination like the UFI dataset.