View transformation-based cross-view gait recognition using transformation consistency measure

Gait features can be extracted from low-quality image sequences captured at a distance, which makes gait recognition a useful method in forensics. However, the accuracy of gait recognition is often degraded in a cross-view setting, which often occurs in forensic cases. Therefore, we propose a gait recognition algorithm that achieves high accuracy in a cross-view setting. In this paper, we focus on a view transformation model-based approach, extract transformation consistency measures, and propose to use these measures for cross-view recognition. To evaluate the accuracy of the proposed method, we draw receiver operation characteristic curves together with Tippett plots, and evaluate discrimination ability and calibration quality. The experimental results show that our proposed method achieves good results in terms of discrimination and calibration.