Towards a view invariant gait recognition algorithm

Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a side-view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera image plane. However, it is not realistic to expect this assumption to be valid in most real-life scenarios. Hence, it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. This is the main theme of the paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: (i) using the perspective projection model; (ii) using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.