title: Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis.
abstract: We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods.
authors.affiliation: Department of Electronic and Computer Engineering, Brunel University, London UB8 3PH, UK. nikolaos.boulgouris@brunel.ac.uk
authors.forename: Nikolaos V
authors.initials: NV
authors.lastname: Boulgouris
authors.forename: Xiaxi
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology*; D006257:Head / Q000502:physiology; D006801:Humans; D007866:Leg / Q000502:physiology; D008390:Markov Chains; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011187:Posture / Q000502:physiology; D060726:Torso / Q000502:physiology; D014741:Video Recording / Q000379:methods*; D016138:Walking / Q000502:physiology