Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplest Representation Yet for Gait Recognition: Averaged Silhouette
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Night Gait Recognition Based on Template Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Statistical feature fusion for gait-based human recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Identification of humans using gait
IEEE Transactions on Image Processing
An efficient gait recognition with backpack removal
EURASIP Journal on Advances in Signal Processing
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Most of gait recognition algorithms involve walking cycle estimation to accomplish signature matching. However, we may be plagued by two cycle-related issues when developing real-time gait-based walker recognition systems. One is accurate cycle evaluation, which is computation intensive, and the other is the inconvenient acquisition of long continuous sequences of gait patterns, which are essential to the estimation of gait cycles. These drive us to address the problem of distant walker recognition from another view toward gait, in the hope of detouring the step of gait cycle estimation. This paper proposes a new gait representation, called normalized dual-diagonal projections (NDDP), to characterize walker signatures and employs a normal distribution to approximately describe the variation of each subject's gait signatures in the statistical sense. We achieve the recognition of unknown gait features in a simplified Bayes framework after reducing the dimension of raw gait signatures based on linear subspace projections. Extensive experiments demonstrate that our method is effective and promising.