Abstract
The goal of gait recognition is to identify a person from a distance based on their walking style using a visual camera. However, the covariates such as a walk with carrying a bag and a change in clothes impact the recognition accuracy. This paper proposed a framework for human gait recognition based on deep learning and Bayesian optimization. The proposed framework includes both sequential and parallel steps. In the first step, optical flow-based motion regions are extracted and utilized to train the fine-tuned EfficentNet-B0 deep model. Simultaneously, we proposed a video frame enhancement technique. The same model is also separately trained on enhanced video frames. Instead of selecting static hyperparameters during the training process, Bayesian Optimization is employed in this work. Following that, two models have obtained: the motion frames model and the original frames model. Features are extracted from both models and fused using a proposed parallel approach named-Sq-Parallel Fusion (SqPF). The Tiger optimization algorithm was then improved and named Entropy controlled Tiger optimization (EVcTO). The best features are finally classified using an extreme learning machine (ELM) classifier. The experiment was carried out on two publicly available datasets, CASIA B and CASIA C, and yielded average accuracy of 92.04 and 94.97%, respectively. The proposed framework outperforms other deep learning-based networks in terms of accuracy.
•Proposed a sequential contrast enhancement technique based on split and fusion.•Initialized hyperparameters of deep model through Bayesian Optimization.•Fused two-stream extracted features using proposed approach named-Sq-Parallel Fusion (SqPF).•Improved tiger optimization algorithm called entropy controlled TO.