Gait Recognition Using CGAN and EfficientNet Deep Neural Networks

Entesar T. Burges, Zakariya A. Oraibi, Ali Wali

Abstract: The objective of gait recognition is to use a visual camera to identify a person from a distance using a visual camera by their distinctive gait. However, the accuracy of this recognition can be impacted by things like carrying a bag and changing clothes. The framework for human gait recognition system presented in this study is based on deep learning and EfficientNet Deep Neural Network. The proposed framework includes three steps. The first step involves extracting silhouettes. The second step involves computing the gait cycle, and the third involves calculating gait energy Depending on the conditional generative adversarial networks and EfficientNet Deep Neural Network. In the first step, silhouette images are extracted using Gaussian mixture-based background algorithm. The segmentation of the gait cycle is estimated by measuring the silhouette’s bounding box’s length and width, then calculating gait energy. Images resulted from the previous stage are used as input to the conditio