Independent component analysis: algorithms and applications
Neural Networks
Face recognition using independent component analysis and support vector machines
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Gait analysis for classification
Gait analysis for classification
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Analysis for Human Identification in Frequency Domain
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Automatic gait recognition via Fourier descriptors of deformable objects
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Gait recognition using independent component analysis
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion
Pattern Recognition Letters
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
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
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This paper proposes an approach to automatic gait recognition based on wavelet descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, the background extraction method is applied to subtract the moving human figures accurately and to obtain binary silhouettes. Secondly, these silhouettes are described with wavelet descriptors and converted into one-dimensional signals to get the independent components (ICs) of these feature signals through ICA. Then, a fast and robust fixed-point algorithm for calculating the ICs is adopted and a selection criterion how to choose ICs is given. Lastly, the nearest neighbor and support vector machine classifiers are chosen for recognition and the method is tested on the XAUT and NLPR gait database. Experimental results show that our method has encouraging recognition accuracy with comparatively low computational cost.