Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Gait Recognition Using Multiple Cameras
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Human Identification by Using the Motion and Static Characteristic of Gait
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Identification of humans using gait
IEEE Transactions on Image Processing
Fusion of static and dynamic body biometrics for gait recognition
IEEE Transactions on Circuits and Systems for Video Technology
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A gait recognition algorithm is proposed that fuses motion and static features of sequences of silhouette images--the wavelet moment and the widths capture the motion and static characteristic of gait. A subspace transformation, Principal Component Analysis(PCA), is applied to process the spatial templates. It aims essentially at reducing data dimensionalities. Finally, nearest neighbor classifier is adopted to recognize subjects. Experimental results show that the method is efficient for human identification, and has a recognition rate of around 88% on the CASIA data set, furthermore, the performance is compared with other algorithms.