A Model (In)Validation Approach to Gait Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Combined Fractal Scale Descriptor for Gait Sequence
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model
VECIMS'09 Proceedings of the 2009 IEEE international conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems
Improved gait recognition by multiple-projections normalization
Machine Vision and Applications
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
Performance prediction for individual recognition by gait
Pattern Recognition Letters
Gait recognition using Hough transform and principal component analysis
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Amplitude spectrum-based gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Dual phase learning for large scale video gait recognition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Combining wavelet velocity moments and reflective symmetry for gait recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
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A gait recognition system using extended template features is presented. A proposed statistical approach is applied for feature extraction from spatial and temporal templates. This method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Dimensionality reduction is achieved by template extraction followed by principal component analysis. Gait recognition is achieved in the canonical space using a measure of accumulated distance as the metric. By incorporating spatial and temporal information into an extended feature, gait recognition becomes more robust and accurate than using spatial or temporal features alone