Enhancement of glove-based approach to dynamic signature verification by reducing number of sensors

Abstract

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online signature verification technique using Singular Value Decomposition (SVD) for signature classification and verification is presented. The proposed technique is based on the SVD in finding r-singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its r-principal subspace, the authenticity is they can be obtained by calculating the angles between the different subspaces. In this paper we try to ponder a significant analysis of accuracy and performance of dynamic signature identification and verification using data glove with reduced number of sensors from 14 to 5 to achieve a significant level of accuracy. The SVD-based signature verification technique is appears to be promising with the best combination of selected 5 prominent sensors instead of select all the 14-sensor based data sets and the best performance is shown to be able to produce 2.33% of Equal Error Rate (EER).