Frontal Gait Recognition From Incomplete Sequences Using RGB-D Camera
Frontal gait recognition using partial cycle information has not received significant attention to date in spite of its many potential applications. In this paper, we propose a hierarchical classification strategy that combines front and back view features captured by RGB-D (Red Green Blue - Depth) cameras. Airport security check points are considered as a typical application scenario, where two depth cameras mounted on top of a metal detector gate positioned beyond a yellow line, respectively, record front and back views of a subject as he goes through the check-in process. Due to the short distance of the surveillance zone between the yellow line and point of exit, it is often not possible to capture a full gait cycle independently from the front view or back view. An initial stage of anthropometric feature-based classification followed by motion feature extraction from the front view is used to restrict the potential set of matched subjects. A final classification is then applied on this reduced set of subjects using depth features extracted from the back view. The method is computationally efficient with a much higher rate of accuracy compared with existing gait recognition approaches.