Human gait recognition by fusing global and local image entropy features with neural networks

We propose a robust gait recognition method based on the combination of global and local image entropy features. An improved feature extraction scheme is developed, in which binary walking silhouettes are characterized with global and local image entropy features. Gait dynamics underlying image entropy features are derived and fused. Additionally, pretrained deep neural networks are employed as the feature extractor on the raw fused image entropy features. The extracted gait dynamics and deep transfer learning features are finally fused and fed into a seven-layer fully connected network for the identification task. The proposed method can make use of global and local gait characteristics sufficiently, which is helpful for resisting walking conditions variation. Experiments on the CASIA-B database are conducted to demonstrate the efficiency of the proposed method. (C) 2022 SPIE and IS&T