A new hand representation based on kernels for hand posture recognition

Abstract

Hand posture recognition is an extremely active research topic in Computer Vision and Robotics, with many applications ranging from automatic sign language recognition to human-system interaction. Recently, a new descriptor for object representation based on the kernel method (KDES) has been proposed. While this descriptor has been shown to be efficient for hand posture representation, across-the-board use of KDES for hand posture recognition has some drawbacks. This paper proposes three improvements to KDES to make it more robust to scale change, rotation, and differences in the object structure. First, the gradient vector inside the gradient kernel is normalized, making gradient KDES invariant to rotation. Second, patches with adaptive size are created, to make hand representation more robust to changes in scale. Finally, for patch-level features pooling, a new pyramid structure is proposed, which is more suitable for hand structure. These innovations are tested on three datasets; the results bring out an increase in recognition rate (as compared to the original method) from 84.4% to 91.2%.