A Novel Gait Recognition System Based on Hidden Markov Models

The advances in computing power, availability of large-capacity storage devices and research in computer vision have contributed to recent developments in gait recognition. The ease of acquiring human videos by low cost equipments makes gait recognition much easier and less intrusive than other biometric systems. In this paper, a gait recognition system using a space-model-based approach is proposed. The proposed mechanism is able to detect moving object of interest, while track them and analyzing their gait for recognition. The system captures videos of subjects in front a stationary camera. The identification module makes use of the shape and dynamics of the system using HMM. Then it models the gait properties by accepting the feature vectors as input and model the dynamics through state transitions and observation probabilities. The experimental results show that the proposed gait recognition system successfully recognizes humans using their gait.