Proposed methodology for gait recognition using generative adversarial network with different feature selectors

Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking manner quickly, as each human has a unique way of walking. The proposed generative adversarial network (GAN) generates gait images from pedestrians of the CASIA and OU-ISIR gait datasets for normalizing the number of frames in each class in both datasets. Then, we extract the gait features from the normalized datasets by the pretrained convolutional neural network (CNN) models; AlexNet, Inception, VGG16, VGG19, ResNet, and Xception, and balance them using the synthetic minority oversampling technique (SMOTE) to enhance the recognition results. Several feature selectors are selecting the best features, including particle swarm optimization (PSO), grey wolf optimization (GWO), Chi-square, and genetic models. Finally, the proposed deep neural network recognizes the gait images precisely. Several performance assessment measures were generated to assess the model's quality, including accuracy, precision, sensitivity, specificity, false negative rate (FNR), intersection of union (IoU), and time. Experimental results showed that the inception model with genetic feature selector performed better in all used datasets than the current studies and was more robust against any change, achieving 99.3%, 99.1%, and 99.09% accuracy in identifying CASIA-B, CASIA-A, and OU-ISIR datasets, respectively in low time.