Gait Recognition Considering Walking Direction

Gait recognition is an important research problem in the field of computer vision. The goal is to identify people by analysis of gait patterns. Because the technique can be performed remotely, it has been applied to access control, surveillance, etc. Most research is based on the assumption that people’s walking direction is perpendicular to the camera axis. In this case the silhouette can be extracted to identify individuals. This limits the application and development of gait recognition. Consequently, walking direction has recently become a popular and challenging research problem. An improved gait recognition approach is proposed. It can give high recognition rates in cases where people’s walking direction is not perpendicular to the camera axis. We describe a novel approach to walking direction computation using information about camera position. The walking direction angle and camera affine projection model are used to define features that can be related to a kinematic model of a human being. Support Vector Machine is used for classification and to evaluate the power of the approach. We apply our method to real human walking image sequences, and achieve relatively high recognition rates. Our approach illustrates how changes in walking direction affect gait parameters in terms of recognition performance. We show that the use of the walking direction algorithm improves recognition rates under variation in viewing direction.