Biometric Authorization System using Gait Biometry

TL;DR

This paper presents a novel hybrid holistic gait recognition system using background subtraction, feature extraction, and SVM classification to identify individuals in surveillance videos, demonstrating high accuracy with RBF kernel.

Contribution

It introduces a hybrid approach combining spatial, temporal, and wavelet gait features with SVM for biometric recognition in security applications.

Findings

SVM with RBF kernel outperforms other kernels in recognition accuracy.

The system effectively recognizes individuals using side view gait videos.

Experimental results show high recognition rates on NLPR database.

Abstract

Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.