Gait Identification Considering Body Tilt byWalking Direction Changes

Gait identification has recently gained attention as a method of identifying individuals at a distance. Thought most of the previous works mainly treated straight-walk sequences for simplicity, curved-walk sequences should be also treated considering situations where a person walks along a curved path or enters a building from a sidewalk. In such cases, person's body sometimes tilts by centrifugal force when walking directions change, and this body tilt considerably degrades gait silhouette and identification performance, especially for widely-used appearance-based approaches. Therefore, we propose a method of body-tilted silhouette correction based on centrifugal force estimation from walking trajectories. Then, gait identification process including gait feature extraction in the frequency domain and learning of a View Transformation Model (VTM) follows the silhouette correction. Experiments of gait identification for circular-walk sequences demonstrate the effectiveness of the proposed method

Gait volume: Spatio-temporal analysis of walking

Abstract. This paper describes a new approach for identifying a person from a spatio-temporal volume that consists of sequential images of a person walking in an arbitrary direction. The proposed approach employs an omnidirectional image sensor and analyzes the three dimensional frequency properties of spatiotemporal volume, because the sensor can capture a long image sequence of a person’s movements in all directions, and, as well, their walking patterns have some cycles. Spatio-temporal volume data, here called “gait volume”, contain information not only of spatial individualities such as features of the torso and face, but also movements of their torso within a unique rhythm. Three dimensional fourier transform is applied to the gait volume to obtain a unique frequency for each person’s walking pattern. This paper also evaluates the availability of three dimensional frequency analysis of the gait volume, how much is the difference in frequency patterns while walking.