GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

Human gait recognition (HGR) is the process of identifying a subject (human) based on their walking pattern. Each subject is a unique walking pattern and cannot be simulated by other subjects. But, gait recognition is not easy and makes the system difficult if any object is carried by a subject, such as a bag or coat. This article proposes an automated architecture based on deep features optimization for HGR. To our knowledge, it is the first architecture in which features are fused using multiset canonical correlation analysis (MCCA). In the proposed method, original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and Efficientnet. Deep transfer learning was used to train both fine-tuned models on selected angles, yielding two new targeted models that were later used for feature engineering. Features are extracted from the deep layer of both fine-tuned models and fused into one vector using MCCA. An improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network classifier. The experimental process was conducted on all 11 angles of the large multi-view gait dataset (CASIA B) dataset and obtained improved accuracy than the state-of-the-art techniques. Moreover, a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.