Robust Gait-Based Gender Classification for Video Surveillance Applications

Authors

C. KalaiSelvan, Department of Electronics & Communication Engineering, Pavendar Bharathidasan College of Engineering & Technology, Trichy, Tamil nadu, IndiaFollow
A. Sivanantha Raja, Department of Electronics & Communication Engineering, Alagappa chettiar College of Engineering & Technology, Karaikudi, Tamil nadu, IndiaFollow

Author Country (or Countries)

India

Abstract

In the paper, the main objective is to develop a gait-based gender classification in video surveillance applications that can run in real time. The Most important contribution of this work is a new fast feature extraction strategy that uses the 2D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and arranged into groups. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. By analysing the discriminatory capability of different body components, it is observed that hair, back, chest and thigh components are more distinct than other components. Then, final discriminative features are computed and KNN tool is used as a classifier. Experiments over auditorium datasets, CASIA-B database and TUM-IITKGP dataset are verified and the proposed method can essential characters determine the target, efficiently label moving targets, and classify the gender.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/110428

Recommended Citation

KalaiSelvan, C. and Sivanantha Raja, A. (2017) "Robust Gait-Based Gender Classification for Video Surveillance Applications," Applied Mathematics & Information Sciences: Vol. 11: Iss. 4, Article 28.
DOI: http://dx.doi.org/10.18576/amis/110428
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss4/28