Multi-view gait recognition with incomplete training data

Abstract

Changes in the viewing angles pose a major challenge for gait recognition because the human gait silhouettes can be different under the various viewing angles. Recently, View Transformation Model (VTM) was proposed to tackle this problem by transforming gait features from across views to a common viewing angle. However, VTM must use the data of subjects crossing all views to train the pre-constructed model, which might be unsuitable for the real applications. To address this problem, this paper proposes a View Feature Recovering Model (VFRM) to generate the VTM with incomplete training data. In our algorithm, if the gait signature of a pedestrian is missing under a view, it can be recovered from the K-nearest pedestrians whose gait features are available in the same view. Moreover, the Geodesic distance based K-Nearest Neighbor (GKNN) algorithm is adopted in our algorithm to better measure the neighborhood between two pedestrians. Experimental results on a benchmark database has demonstrated the effectiveness of our method.