Frontal gait recognition based on spatio-temporal interest points

Abstract

This paper proposes a spatio-temporal interest point (STIP)-based human gait recognition method for frontal gait videos. The proposed method extracts histograms of oriented gradient (HOG) for STIPs directly from gait videos without involving a gait cycle segment, which is required by silhouette analysis as used by most gait recognition methods. Moreover, silhouette extraction may be affected by noise and carried objects leading to consequent recognition error. Matches of STIPs between two gait videos are then found in terms of HOG to measure the similarity of two videos so as to achieve the goal of gait recognition. The experimental results offer evidence that our method outperforms the existing methods on the Carnegie-Mellon University Motion of Body database and the Chinese Academy of Sciences Institute of Automation data-set B.

Keywords:

• Frontal gait recognition
• spatio-temporal interest points
• histogram of oriented gradient
• individual identification

Acknowledgments

The authors would like to thank R. Gross and J. Shi for providing CMU MoBo database, S. Yu, D. Tan, and T. Tan for providing CASIA database, and the anonymous reviewers for the valuable and insightful comments on the earlier version of this manuscript.