Combine hierarchical appearance statistics for accurate palmprint recognition

Palmprint recognition is an active member of biometrics in recent years. State-of-the-art algorithms of palmprint recognition describe appearances of palmprints efficiently through local texture analysis. Following this framework, we propose a novel approach of palmprint recognition in this paper, which represents palmprint images based on statistics and spatial arrangement of appearance descriptors within local image areas. In this method, we firstly design a robust descriptor to encode properties of palmprint appearances of local regions. The whole image is divided into non-overlapped blocks at increasingly fine resolutions successively, so as to describe the spatial layout in hierarchical scales. For a specific spatial resolution, local distributions of the proposed descriptors in the blocks are concatenated to represent structures of palmprint structures. Finally, distribution information of different resolutions is combined to provide complementary descriptive power. Promising experimental results demonstrate that the proposed method achieves even better performances than the state-of-the-art approaches. 1