Palm-Print Classification by Global Features

Three-dimensional (3-D) palm print has proved to be a significant biometrics for personal authentication. Three-dimensional palm prints are harder to counterfeit than 2-D palm prints and more robust to variations in illumination and serious scrabbling on the palm surface. Previous work on 3-D palm-print recognition has concentrated on local features such as texture and lines. In this paper, we propose three novel global features of 3-D palm prints which describe shape information and can be used for coarse matching and indexing to improve the efficiency of palm-print recognition, particularly in very large databases. The three proposed shape features are maximum depth of palm center, horizontal cross-sectional area of different levels, and radial line length from the centroid to the boundary of 3-D palm-print horizontal cross section of different levels. We treat these features as a column vector and use orthogonal linear discriminant analysis to reduce their dimensionality. We then adopt two schemes: 1) coarse-level matching and 2) ranking support vector machine to improve the efficiency of palm-print recognition. We conducted a series of 3-D palm-print recognition experiments using an established 3-D palm-print database, and the results demonstrate that the proposed method can greatly reduce penetration rates.