Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Abstract

Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance.

Keywords

• Face recognition
• local gradient number pattern
• fuzzy convex-concave partition
• LDP-based methods