Cross-view gait recognition based on a restrictive triplet network

To overcome the influence of view variations, a restrictive triplet network (RTN) is proposed to solve the problem of cross-view gait recognition in this paper. This network comprises five shared convolutional layers. The restrictive triplet loss is adopted to optimize RTN based on the triplet-based sample groups. These gait samples are selected by a special strategy, so as to make RTN converges faster. The model optimized by this method is adopted to extract the view-invariant feature for cross-view gait recognition. Besides, two additional networks named BDN and TDN are proposed to compare with RTN, which match the adjacent features at different convolutional layers. Finally, extensive evaluations are conducted based on the CASIA-B, OU-ISIR and USF dataset. Experimental results indicate that RTN performs best. Besides, the state-of-the-art methods are selected to compare with RTN. Among them, RTN achieves the best recognition score, which reaches 94.62% under singe view angle and 91.68% under cross-view angle, respectively. The results demonstrate that RTN is robust against view variations, which shows the great potential of RTN for practical applications in the future. (C) 2019 Elsevier B.V. All rights reserved.