A Framework for Saudi Uniform Gait Recognition Based on Kinect Skeletal Tracking

Abstract

Due to its robustness in challenge variation in gait recognition domain, gait recognition is considered as one of the popular remote biometric identification technologies. Gait data may be reliably collected from a long distance and is difficult to conceal or copy. This article investigated the use of Kinect to identify gait in Saudis wearing loose-fitting apparel that conceals the majority of body shapes, such as thobes or abayas. Because these clothes cover the majority of the joints, it is difficult to determine gait. This research uses the Kinect sensor version 2 as a technique to choose the top three joints with the greatest identification results, which are then used for gait recognition. The Y coordinates of joints are used as features, which are then put into the K Nearest Neighbor classification algorithm. Several experiments were carried out, and the results demonstrate that the system has a promising identification rate and is capable of achieving a high recognition performance when identifying or recognizing a person while also dealing with obstacles associated with the types of loose clothing worn by the participants.