Signature identification via efficient feature selection and GPU-based SVM classifier

The problem of handwritten signature recognition is considered significant in biometrics, in particular for determining the validity of official documents. The rationale consists of creating an off-line classifier to discriminate between fake (forged) and genuine digitalized signatures. In such applications containing thousands of samples machine learning techniques such as Support Vector Machines (SVM) play a preponderant role in overcoming the challenges inherent to this problematic. However, to deal with the computational burden of calculating the large Gram matrix, approaches such as Graphics Processing Units (GPU) computing are required for efficiently processing big image biometric data. In this paper, first, we present an empirical study for efficient feature selection concerning the signature identification problem. Second, an GPU-based SVM classifier that integrates a component of the open source Machine Learning Library (GPUMLib) supporting several kernels is developed. Third, we ran several experiments with improved performance over baseline approaches. From our study, we gain insights in both performance and computational cost under a number of experimental conditions, and conclude that the most appropriate model is usually a trade-off between performance and computational cost for a given experimental setup and dataset.