Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Unsupervised Learning Motion Models Using Dynamic Time Warping
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Gait analysis for human identification through manifold learning and HMM
Pattern Recognition
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
IEEE Transactions on Audio, Speech, and Language Processing
Human identification based on gait paths
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
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The scope of this article is gait identification of individuals on the basis of reduced sequences of video recordings data. The gait sequences are considered to be the 3rd-order tensors and its dimensionality is reduced by Multilinear Principal Component Analysis with different values of variation covered. Reduced gait descriptors are identified by the supervised classifiers: Naive Bayes and Nearest Neighbor. CASIA Gait Database 'dataset A' is chosen to verify the proposed method. The obtained results are promising. For the Naive Bayes and attributes discretization almost 99% of classification accuracy is achieved, which means only one misclassified gait out of eighty validated.