Visual Face Recognition Using Bag of Dense Derivative Depth Patterns

Abstract

A novel biometric face recognition algorithm using depth cameras is proposed. The key contribution is the design of a novel and highly discriminative face image descriptor called bag of dense derivative depth patterns (Bag-D3P). This descriptor is composed of four different stages that fully exploit the characteristics of depth information: 1) dense spatial derivatives to encode the 3-D local structure; 2) face-adaptive quantization of the previous derivatives; 3) multibag of words that creates a compact vector description from the quantized derivatives; and 4) spatial block division to add global spatial information. The proposed system can recognize people faces from a wide range of poses, not only frontal ones, increasing its applicability to real situations. Last, a new face database of high-resolution depth images has been created and made it public for evaluation purposes.