Palm vein recognition based on a modified $$\text{(2D)}^{2}\text{ LDA}$$

Abstract

Biometric computing offers an effective approach to personal identification based on unique, stable physical or behavioral characteristics. A reliable and robust personal verification approach using palm vein patterns is presented in this paper. This approach has lower computational and memory requirements and a higher recognition accuracy than similar methods. In my work, a near-infrared charge-coupled device camera is adopted as an input device for capturing palm vein images, since it can provide low-cost, non-contact imaging. In the proposed approach, two finger-webs are automatically selected to define the region of interest (ROI) in the palm vein images. Modified two-directional two-dimensional linear discriminant analysis (\(\text{(2D)}^{2}\text{ LDA}\)), which performs an alternate two-dimensional LDA (2DLDA) in the column direction of images in the 2DLDA subspace, is proposed to exploit the correlation between rows and columns of palm vein features inside the ROI. The major advantage of the method is that it requires fewer coefficients for efficient palm vein image representation and recognition. A total of 4,140 palm vein images were collected from 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracies (\(>\)99 %) have been obtained by the proposed method, and the speed of the method (response time \(<\)0.75 s) is rapid enough for real-time recognition. Experimental results demonstrate that the proposed approach is feasible and effective for palm vein recognition.