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.
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Acknowledgments
We would like to express our warm gratitude to Prof Yasushi Yagi and his entire research team of Osaka University, Japan for providing us OU-ISIR Gait database to accomplish our research work.
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Nandy, A., Bhowmick, S., Chakraborty, P., Nandi, G.C. (2014). Gait Biometrics: An Approach to Speed Invariant Human Gait Analysis for Person Identification . In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_78
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DOI: https://doi.org/10.1007/978-81-322-1602-5_78
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