Discriminative feature selection for on-line signature verification

On-line handwritten signatures are collected as real-time dynamical signals which are written on collective devices by users. Since individuals have different writing habits, consistent and discriminative features should be selected to distinguish genuine signatures from forged signatures. In this paper, two methods, which are based on full factorial experiment design and optimal orthogonal experiment design, are proposed for selecting discriminative features among candidates. To improve the robustness, consistency of feature is analyzed at first, and more consistent features are selected as candidates for discriminative feature selection. To reduce the influences of fluctuations caused by internal and external writing environments changes before verification, signatures are effectively aligned to their reference templates based on Gaussian mixture model. A modified dynamic time warping with signature curve constraint is presented for verification to improve the efficiency. Comprehensive experiments are implemented based on the data of the open access databases MCYT and SVC2004 Task2. Experimental results verify the effectiveness and robustness of our proposed methods.