Gait Verification System Through Multiperson Signature Matching for Unobtrusive Biometric Authentication

The unobtrusive nature of gait facilitates the development of optimal biometric authentication systems. Recent approaches on video-analytic gait authentication show excellent results but their implementations are threshold-based which trade off a set amount of FAR (false acceptance rate) to produce an acceptable FRR (false rejection rate). The proposed multiperson signature mapping (MSM) approach overcomes this drawback with a design that substantially decreases the FAR of the authentication system without having to increase the FRR. This technique removes the need of an empirically adjusted threshold. The state-of-the-art algorithms mostly prefer the nearest neighbor (NN) classifier where the Euclidean distance calculated from the extracted feature hyperplane is taken as the similarity measure. Our study proves that the Bayes’ rule applied over the extracted feature set provides a much better performance compared to the conventional NN approach. The MSM is applied on top of template-based gait recognition algorithms to produce an efficient gait authentication system. The method is evaluated on four different gait templates including the popular Gait Energy Image (GEI) and its variation with the genetic template segmentation (GTS). The study analyzes the performance across different clothing and carrying conditions. The deployment of the gait authentication system for practical application is explained in detail. Experimental results with the CASIA-B gait database depict the potential of our proposed approach.