HGANet-23: a novel architecture for human gait analysis based on deep neural network and improved satin bowerbird optimization.

Human gait is an essential biometric feature in the area of computer vision research. Over the past ten years, there has been a growing demand for a non-contact biometric approach to identify potential candidates, mainly since the global COVID-19 epidemic emerged. Gait recognition involves automatically capturing and extracting characteristics of human movement, which are subsequently utilized to verify the identity of a moving individual. Nevertheless, covariates like walking while carrying a bag, changing clothes, environmental conditions, and any unusual gait patterns all have an impact on the accuracy of gait recognition accuracy. This paper presents a new end-to-end deep learning framework for human gait recognition. The proposed framework contains a few important steps that help in the improvement of the recognition accuracy. A contrast enhancement technique named Enhancing Human Body Shape and Reducing Noise is proposed at the initial step and used for the dataset augmentation. The second step involves deep learning architecture development, such as the proposed GNET-23 model and a fine-tuned pre-trained AlexNet model. Both models are trained on selected datasets and later extract deep features from the average pooling layer. A novel parallel correlation fusion technique is proposed to fuse the richer information of both models that are further optimized using an improved Satin Bowerbird optimization algorithm. Finally, the most optimal features are classified using Neural Networks and nearest-neighbor classifiers. The experiment was conducted using four different angles of publicly accessible CASIA-B datasets, resulting in mean accuracy scores of 91.6%, 96.2%, 94.3%, and 96.8%, respectively. The proposed framework surpasses other deep learning networks and recently published techniques in both accuracy and processing speed. [ABSTRACT FROM AUTHOR]