Human hand identification with 3D hand pose variations

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, San Francisco, CA, 13-18 June 2010Several recent research efforts in the hand biometrics have focused on developing contact-free personal identification system. However, the acquisition of images from the unconstrained hands can introduce significant hand pose variations which severely limits the performance and applicability of these approaches. This paper presents a new approach to achieve significantly improved performance even in the presence of large hand pose variations. The proposed approach firstly estimates the orientation of the hands in 3D space and then attempts to normalize the pose of the simultaneously acquired 3D and 2D hand images. A new feature representation, namely SurfaceCode, is proposed for matching a pair of 3D palms. Multimodal (2D as well as 3D) palmprint and hand geometry features, which are simultaneously extracted from the texture details of the normalized 3D hands, are used for the matching. Individual matching scores are then consolidated using the weighted sum rule. Our experiments on the database of 114 subjects, with significant pose variations, achieve consistent performance improvement, both for palmprint and hand geometry features considered in this work.Department of ComputingRefereed conference pape

Method and system for personal identification using 3D palmprint imaging

US8265347; US8265347 B2; US8265347B2; US8,265,347; US 8,265,347 B2; 8265347; Appl. No. 12/428,039Inventor name used in this publication: David ZhangUSVersion of Recor

Robust blurred palmprint recognition via the fast Vese-Osher model

In this paper, we propose a new palmprint recognition system by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to extract the structure layer and texture layer of blurred palmprint images by using the fast Vese-Osher decomposition model, and the structure layer is proved to be more stable and robust than texture layer for palmprint recognition. Second, a novel algorithm based on weighted robustness with histogram of oriented gradient (WRHOG) is proposed to extract robust features from the structure layer of blurred palmprint images, which can address the problem of translation and rotation to a large extent. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the new recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness of the proposed recognition system