Subspace-based signature verification technique using reduced-sensor data glove

Online signature verification is a dynamic method in which the biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The system make used of a data glove which is originally designed for virtual reality application as an input device. The proposed technique for online signature verification is based on the singular value decomposition (SVD) technique which involves four aspects: 1) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. In this paper, we investigate the performance of the signature verification system with reduced-sensor data glove. The selection of the most prominent sensors of the data glove is based on the F-value for each sensor. The signature verification technique was tested with large number of authentic and forgery signatures and has demonstrated that it has the potential to offer high level of security for special application, such as banking and electronic commerce.