EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Gait Analysis for Recognition and Classification
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
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A new method for human gait recognition using temporal analysis
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Automatic Gait Recognition Using Weighted Binary Pattern on Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
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This paper presents a new method for human model-free gait recognition using principal curves analysis and neural networks. Principal curves are non-parametric, nonlinear generalizations of principal component analysis, and give a breakthrough to nonlinear principal component analysis. Different from the traditional statistical analysis methods, principal curve analysis seeks lower-dimensional manifolds for every class respectively, and forms the nonlinear summarization of the sample features and directions for each class. Neural network with the virtue of its universal approximation property is an outstanding method to model the nonlinear function of principal curve. Firstly, a background subtraction is used to separate objects from background. Secondly, we extract the contour of silhouettes and represent the spatio-temporal features. Finally, we use principal curves and neural networks to analyze the features to train and test gait sequences. Recognition results demonstrate that our method has encouraging recognition performance.