International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal dimensionality of metric space for classification
Proceedings of the 24th international conference on Machine learning
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2007 papers
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH Asia 2008 papers
Neighbor embedding based super-resolution algorithm through edge detection and feature selection
Pattern Recognition Letters
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Technical Section: Hyper-Resolution: Image detail reconstruction through parametric edges
Computers and Graphics
Efficient graphical models for processing images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Gait Recognition Using Radon Transform and Linear Discriminant Analysis
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
IEEE Transactions on Circuits and Systems for Video Technology
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Frontal view gait based recognition using PCA
ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
Greedy regression in sparse coding space for single-image super-resolution
Journal of Visual Communication and Image Representation
Gait recognition based on shape and motion analysis of silhouette contours
Computer Vision and Image Understanding
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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Unlike other biometric authentication methods, gait recognition is noninvasive and effective from a distance. However, the performance of gait recognition will suffer in the low-resolution (LR) case. Furthermore, when gait sequences are projected onto a nonoptimal low-dimensional subspace to reduce the data complexity, the performance of gait recognition will also decline. To deal with these two issues, we propose a new algorithm called superresolution with manifold sampling and backprojection (SRMS), which learns the high-resolution (HR) counterparts of LR test images from a collection of HR/LR training gait image patch pairs. Then, we incorporate SRMS into a new algorithm called multilinear tensor-based learning without tuning parameters (MTP) for LR gait recognition. Our contributions include the following: 1) With manifold sampling, the redundancy of gait image patches is remarkably decreased; thus, the superresolution procedure is more efficient and reasonable. 2) Backprojection guarantees that the learned HR gait images and the corresponding LR gait images can be more consistent. 3) The optimal subspace dimension for dimension reduction is automatically determined without introducing extra parameters. 4) Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF human gait database and the CASIA gait database show the increased efficiency of the proposed algorithm, compared with previous algorithms.