Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
A full-body layered deformable model for automatic model-based gait recognition
EURASIP Journal on Advances in Signal Processing
Computing and evaluating view-normalized body part trajectories
Image and Vision Computing
Gait recognition based on the feature fusion
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Human attributes from 3D pose tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Appearance-Based gait recognition using independent component analysis
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Gait recognition using hidden markov model
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A new combinatorial approach to supervised learning: application to gait recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Unsupervised clustering of people from 'skeleton' data
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
Factorial hidden Markov models for gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Gait recognition via optimally interpolated deformable contours
Pattern Recognition Letters
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The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel 2-step, model-based approach to gait recognition by employing a 5-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data-set and CMU MoBo data-set, and achieved recognition rate of 61% and 96%, respectively. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera.