Human gait recognition based on hybrid-dimensional features from infrared motion images

Gait recognition, also called gait-based human identification, is a relatively new research direction in biometrics. It aims to discriminate individuals by the way they walk. This paper describes a human recognition algorithm by combining three-dimensional and two-dimensional features of the infrared gait. The similarity of the human models and image was measured using the pose evaluation function which included the boundary and region characteristic. A hierarchical search strategy was used to extract the lower body joint angles. And then the peak values of Radon transform from 2D human silhouettes were also attained. Finally, we carry out the human infrared gait recognition based on SVM using the hybrid-dimensional features. Multiple feature fusion is also executed at feature level, and the recognition results demonstrate that the performance of multiple features is better than any single feature.