Gait biometrics: An approach to speed invariant human gait analysis for person identification

Abstract

A simple and a common human gait can provide an interesting behavioral biometric feature for robust human identification. The human gait data can be obtained without the subject’s knowledge through remote video imaging of people walking. In this paper we apply a computer vision-based technique to identify a person at various walking speeds, varying from 2 km/hr to 10 km/hr. We attempt to construct a speed invariance human gait classifier. Gait signatures are derived from the sequence of silhouette frames at different gait speeds. The OU-ISIR Treadmill Gait Databases has been used. We apply a dynamic edge orientation histogram on silhouette images at different speeds, as feature vector for classification. This orientation histogram offers the advantage of accumulating translation and orientation invariant gait signatures. This leads to a choice of the best features for gait classification. A statistical technique based on Naïve Bayesian approach has been applied to classify the same person at different gait speeds. The classifier performance has been evaluated by estimating the maximum likelihood of occurrences of the subject.