Depth video-based gait recognition for smart home using local directional pattern features and hidden Markov model

Gait recognition at smart home is considered as a primary function of the smart system nowadays. The significance of gait recognition is high especially for the elderly as gait is one of the basic activities to promote and preserve their health. In this work, a novel method was proposed for human gait recognition by processing depth videos from a depth camera. The gait recognition method utilizes local directional patterns (LDPs) for local feature extraction from depth silhouettes and hidden Markov models (HMMs) for recognition. The LDP features were first extracted from the depth silhouettes of a human body from each frame of a video containing human gait. The dimension of the LDP features was reduced by principal component analysis. Then, each HMM was trained using the LDP features. Finally, the recognition was done with a maximum likelihood calculation of the trained HMMs of different gaits. We focused on training and recognizing two kinds of gaits here, namely, normal and abnormal. The proposed approach shows superior recognition performance over other traditional methods of gait recognition.