Fusion of Palmprint and PaLM Vein Images for Person Recognition Based on "Laplacianpalm" Feature

Abstract

Unimodal analysis of palmprint and palm vein has been investigated for person recognition. However, they are not robust to noise and spoof attacks. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with fusion applied at the image level. The palmprint and palm vein images are fused by a novel integrated line-preserving and contrast-enhancing fusion method. Based on our proposed fusion rule, the modified multiscale edges of palmprint and palm vein images are combined as well as the image contrast and the interaction points (IPs) of the palmprints and vein lines are enhanced. The IPs are novel features obtained in our fused images. A novel palm representation, called "Laplacianpalm" feature, is extracted from the fused images by Locality Preserving Projections (LPP). We compare the recognition performance using the unimodal and the proposed fused images. We also compared the proposed "Laplacianpalm " approach with the Fisherpalm and Eigenpalm on a large dataset. Experimental results show that the proposed multimodal approach provides a better representation and achieves lower error rates in palm recognition.