HMM-Based Gait Recognition with Human Profiles

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. We propose a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the hidden Markov model (HMM). Two types of gait models have been developed both with a cyclic architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model’s parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.