Gait Appearance for Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Automatic Gait Recognition by Symmetry Analysis
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Automatic gait recognition by symmetry analysis
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outdoor recognition at a distance by fusing gait and face
Image and Vision Computing
Gait analysis for human identification through manifold learning and HMM
Pattern Recognition
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Self-calibrating view-invariant gait biometrics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Statistical feature fusion for gait-based human recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Statistical recognition techniques have already been shown to achieve good performance in automatic gait recognition. However, the metrics were only statistical in nature and did not describe the intimate nature of gait. Accordingly, new velocity moments have been developed to describe an object and its motion throughout an image sequence. These moments are an extended form of centralized moments and compute descriptions of the object and its behavior.Evaluation shows that the velocity moments have the required descriptive capability, and analysis on synthetic imagery shows that the velocity moments are less sensitive to noise than an averaged comparator moment. This is largely due to the integration of data from the whole sequence. An extraction procedure has been developed to find moving human subjects and we are currently evaluating the performance of this promising new approach in automatic gait recognition.