Extended Model-Based Automatic Gait Recognition of Walking and Running
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Human Identification by Spatio-Temporal Symmetry
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Gait recognition using principal curves and neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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Human gait recognition is the process of identifying individuals by their walking manners. The gait as one of newly coming biometrics has recently gained more and more interests from computer vision researchers. In this paper, we propose a new method for model-free recognition of gait based on silhouette in computer vision sequences. The silhouette shape is represented by a novel approach which includes not only the spatial body contour but also the temporal information. First, a background subtraction is used to separate objects from background. Then, we represent the spatial shape of walker and their motion by the temporal matrix, and use Discrete Fourier analysis to analyze the gait feature. The nearest neighbor classifier is used to distinguish the different gaits of human. The performance of our approach is tested using different gait databases. Recognition results show this approach is efficient.