Title: A speed invariant human identification system using gait biometrics

Authors: Soumabha Bhowmick; Anup Nandy; Pavan Chakraborty; G.C. Nandi

Addresses: Indian Institute of Information Technology Allahabad, Robotics and AI Laboratory, Allahabad, Uttar Pradesh – 211012, India ' Indian Institute of Information Technology Allahabad, Robotics and AI Laboratory, Allahabad, Uttar Pradesh – 211012, India ' Indian Institute of Information Technology Allahabad, Robotics and AI Laboratory, Allahabad, Uttar Pradesh – 211012, India ' Indian Institute of Information Technology Allahabad, Robotics and AI Laboratory, Allahabad, Uttar Pradesh – 211012, India

Abstract: Can gait biometrics be used for identification of a person? We feel that each individual has an intrinsic gait behaviour, irrespective of the individual's gait speed. The challenge is to extract this gait behaviour from the gait biometrics. In this paper, we used a computer vision-based technique for gait identification. The silhouette treadmill gait database obtained from OU-ISIR, Japan has been used in this gait research work. We have used 22 subjects walking at different speeds varying from 2 km/hr to 6 km/hr with speed variation of 1 km/hr. The gait energy image (GEI) has been computed from this gait data. The width of GEI, along the horizontal axis, has been used as the feature vector for training and testing. These features show speed invariance but is intrinsic and unique to the subject. The feature captures the intrinsic hand movement, head node and leg oscillations of the subjects. A probabilistic model based on Bayes' conditional probability rule and connectionist model based on multilayer perceptron neural network have been used for classification. This technique provides a promising result of identifying a subject invariant of the gait speed.

Keywords: human gait; gait cycle; gait energy image; GEI; Bayes conditional probability rule; OU-ISIR gait database; artificial neural networks; ANNs; multilayer perceptron; speed invariance; human identification; gait biometrics; gait behaviour; walking speeds; hand movement; head node; leg oscillations; probabilistic modelling.

DOI: 10.1504/IJCVR.2014.059356

International Journal of Computational Vision and Robotics, 2014 Vol.4 No.1/2, pp.3 - 22

Received: 13 Mar 2013
Accepted: 13 Jul 2013
Published online: 21 Jun 2014 *

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