Finding Identity through Gait

Human gait identification is the recognition of a person from a series of walking images. In contrast to fingerprint or iris-based identification methods, gait identification offers the significant benefit of remote execution. Gait detection is emerging as one of the most promising biometric identification techniques. Traditional methods to identify the identity of gait are Background subtraction, Gait Energy Image, Gait Entropy, etc., human gait identification is a potential new tool for identifying individuals beyond traditional methods. The objectives of this study were to develop an automated gait detection system and examine the unique aspects of gait. This study encompassed the following: gait predictions and estimation based on HRNet 17 joint positions (locating body joints of humans in an image/video), better predictions with higher confidence scores by eliminating poor predictions, and the 14 joint coordinates and 17 joint points are fed into Graph Convolutional Network by eliminating low confidence pose to classify labels. Semi-supervised learning on graph-structured data can be accomplished via Graph Convolutional Networks. Graph Convolutional Networks is a very effective version of convolutional neural networks, which function directly on graphs. The experimental results show that the Graphical Convolutional Network identifies gait subjects with higher accuracy of normal walking and carrying a backpack with 98% and 96% respectively. This work will be extended in the future by fusing face and gait to identify various gait subjects and the gait in forensic identifications.