Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition by Kinematic-Based Gait Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
On Probabilistic Combination of Face and Gait Cues for Identification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Tracking of Persons in Monocular Image Sequences
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Performance analysis for gait in camera networks
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Gait feature subset selection by mutual information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Performing content-based retrieval of humans using gait biometrics
Multimedia Tools and Applications
Performance analysis for automated gait extraction and recognition in multi-camera surveillance
Multimedia Tools and Applications
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Hi-index | 0.00 |
Both the human motion characteristics and body part measurement are important cues for human recognition at a distance. The former can be viewed as kinematic measurement while the latter is stationary measurement. In this paper, we propose a kinematic-based approach to extract both kinematic and stationary features for human recognition. The proposed approach first estimates 3D human walking parameters by fitting the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Kinematic and stationary features are then extracted from the kinematic and stationary parameters, respectively, and used for human recognition separately. Next, we discuss different strategies for combining kinematic and stationary features to make a decision. Experimental results show a comparison of these combination strategies and demonstrate the improvement in performance for human recognition.