Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplest Representation Yet for Gait Recognition: Averaged Silhouette
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Circuits and Systems for Video Technology
Chrono-gait image: a novel temporal template for gait recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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The small sample size problem and the difficulty in determining the optimal reduced dimension limit the application of subspace learning methods in the gait recognition domain. To address the two issues, we propose a novel algorithm named multi-linear tensor-based learning without tuning parameters ( MTP ) for gait recognition. In MTP , we first employ a new method for automatic selection of the optimal reduced dimension. Then, to avoid the small sample size problem, we use multi-linear tensor projections in which the dimensions of all the subspaces are automatically tuned. Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF Human Gait Database show promising results of MTP compared to other gait recognition methods.