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-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Automatic gait recognition using area-based metrics
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Gait Analysis for Human Identification in Frequency Domain
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Personal authentication using multiple palmprint representation
Pattern Recognition
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 wavelet descriptors and independent component analysis
ISNN'06 Proceedings of the Third international conference on Advnaces 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
IEEE Transactions on Neural Networks
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Factorial HMM and parallel HMM for gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
Fast communication: Active energy image plus 2DLPP for gait recognition
Signal Processing
Computers and Industrial Engineering
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Pattern Recognition Letters
Human gait recognition via deterministic learning
Neural Networks
Activity-based person identification using sparse coding and discriminative metric learning
Proceedings of the 20th ACM international conference on Multimedia
Human-centric indoor environment modeling from depth videos
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
Pipeline-Architecture Based Real-Time Active-Vision for Human-Action Recognition
Journal of Intelligent and Robotic Systems
International Journal of Mobile Learning and Organisation
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This paper proposes a gait recognition method using multiple gait features representations based on independent component analysis (ICA) and genetic fuzzy support vector machine (GFSVM) for the purpose of human identification at a distance. Firstly, the moving human figures are subtracted using simple background modeling to obtain binary silhouettes. Secondly, these silhouettes are characterized with three kinds of gait representations including Fourier descriptor, wavelet descriptor and pseudo-Zernike moment. Then, ICA and GFSVM classifier are chosen for recognition and the method is tested on two gait databases. Comparative performance between these feature representations is investigated and better performance has been achieved than either one individually. Meanwhile, one multiple views fusion recognition approach on the decision level based on product of sum (POS) rule is introduced to overcome the limitation of most single view recognition methods, which achieves better performance than the traditional rank-based fusion rules. Experimental results show that our method has encouraging recognition accuracy.