Stride and Cadence as a Biometric in Automatic Person Identification and Verification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Baseline Results for the Challenge Problem of Human ID Using Gait Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Modelling the effects of walking speed on appearance-based gait recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern 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
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
For human identification at distance (HID) applications, gait characteristics are hard to conceal and has the inherent merits such as non-contact and unobtrusive. In this paper, a novel appearance-based method for automatic gait recognition is proposed using independent component analysis (ICA). Principal component analysis (PCA) is performed on image sequences of all persons to get the uncorrelated PC coefficients. Then, ICA is performed on the PC coefficients to obtain the more independent IC coefficients. The IC coefficients from the same person are averaged and the mean coefficients are used to represent individual gait characteristics. For improving computational efficiency, a fast and robust method named InfoMax algorithm is used for calculating independent components. Gait recognition performance of the proposed method was evaluated by using CMU MoBo dataset and USF Challenge gait dataset. Experiment results show the efficiency and advantages of the proposed method.