Peg Free Multi-Dimensional Palmprint Feature Extraction Using Hybrid Level Fusion Strategy

Abstract

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palm print and geometrical features of the hand. The geometrical features are obtained from the binaries images by using Otsu’s threshold technique. Multimodal (2-D and 3D) palm print and hand geometry features, which are simultaneously extracted from the user’s textured 2-D and 3-D hand, are used for matching. Individual matching scores are then combined using a new Hybrid level fusion strategy. The objective of this work is to improve accuracy and robustness of existing palm print authentication systems using combination of the 2D and 3D palm print features. A peg-free system is composed by a pc and camera. The users put the hand in free space in front of the camera. The hand is illuminated by an infra- red light to solve segmentation problems in a real environment. The surface curvature feature based method is investigated for Gabor feature based competitive coding scheme is used for 2D representation. The database of 120 subjects achieved significant improvement in performance with the integration of 2D and 3D palm print and hand geometry features. Such as those mounted on a laptop, mobile device, and web camera or those for surveillance, can dramatically increase the applicability of such a system. However, the performance of existing techniques for palm print authentication falls considerably, when the camera is not aligned with the surface of the palm. The experimental results also suggest that the Hybrid level fusion approach employed in this work helps to achieve the performance improvement of 70% (in terms of EER) over the case when matching scores are combined using the Dynamic fusion approach.