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Uncooperative gait recognition by learning to rank
Authors:Raúl Martín-Félez  Tao Xiang
Institution:1. Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain;2. School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
Abstract:Gait is a useful biometric because it can operate from a distance and without subject cooperation. However, it is affected by changes in covariate conditions (carrying, clothing, view angle, etc.). Existing methods suffer from lack of training samples, can only cope with changes in a subset of conditions with limited success, and implicitly assume subject cooperation. We propose a novel approach which casts gait recognition as a bipartite ranking problem and leverages training samples from different people and even from different datasets. By exploiting learning to rank, the problem of model over-fitting caused by under-sampled training data is effectively addressed. This makes our approach suitable under a genuine uncooperative setting and robust against changes in any covariate conditions. Extensive experiments demonstrate that our approach drastically outperforms existing methods, achieving up to 14-fold increase in recognition rate under the most difficult uncooperative settings.
Keywords:Gait recognition  Covariate conditions  Learning to rank  Transfer learning  Distance learning
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