Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human gait recognition at sagittal plane
Image and Vision Computing
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Automatic Gait Recognition Using Weighted Binary Pattern on Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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
Gait recognition based on fusion of multi-view gait sequences
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Hierarchical pose estimation for human gait analysis
Computer Methods and Programs in Biomedicine
Unsupervised learning in body-area networks
Proceedings of the Fifth International Conference on Body Area Networks
Reducing the effect of noise on human contour in gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
| Hi-index | 0.00 |
The intimate relationship between human walking and running lies within the skeleto-muscular structure. This is expressed as a mapping that can transform computer vision derived gait signatures from running to walking and vice versa, for purposes of deployment in gait as a biometric or for animation in computer graphics. The computer vision technique can extract leg motion by temporal template matching with a model defined by forced coupled oscillators as the basis. The (biometric) signature is derived from Fourier analysis of the variation in the motion of the thigh and lower leg. In fact, the mapping between these gait modes clusters better than the original signatures (of which running is the more potent) and can be used for recognition purposes alone, or to buttress both of the signatures. Moreover, the two signatures can be made invariant to gait mode by using the new mapping.