Human Identification Using Gait Skeletal Joint Distance Features

Gait not only defines the way a person walks, but also provides insights on an individual's daily routine, mental state or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, authors develop a biometric security system using gait-based skeletal information obtained from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the joint distance of left and right ankles. Authors have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle. After mean and variance features are extracted from the distance feature vector, the KNN algorithm is used for classification purpose. The classification accuracy of the authors' approach is 93.33%. Experimental results show that the proposed approach achieves better recognition accuracy then other state-of-the-art approaches. Incorporating gait biometric in a situation awareness system for identification of a mental state is one of the future directions of this research.