Feature-level fusion of palmprint and palm vein for person identification based on a "Junction Point" representation

The issue of how to represent the palm features for effective classification is still an open problem. In this paper, we propose a novel palm representation, the “Junction Points” (JP) set, which is formed by the two set of line segments extracted from the registered palmprint and palm vein images respectively. Unlike the existing approaches, the JP set, containing position and orientation information, is a more compact feature that significantly reduces the storage requirement. We compare the proposed JP approach with the line-based methods on a large dataset. Experimental results show that the proposed JP approach provides a better representation and achieves lower error rate in palm verification.