Journal ArticleDOI
Joint dynamic sparse representation for multi-view face recognition
TLDR
The proposed joint dynamic sparsity prior promotes shared joint sparsity patterns among the multiple sparse representation vectors at class-level, while allowing distinctSparsity patterns at atom-level within each class to facilitate a flexible representation.About:
This article is published in Pattern Recognition.The article was published on 2012-04-01. It has received 101 citations till now. The article focuses on the topics: Sparse approximation & Facial recognition system.read more
Citations
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Journal ArticleDOI
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
TL;DR: Considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed and demonstrates the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.
Journal ArticleDOI
SAR Automatic Target Recognition Based on Multiview Deep Learning Framework
TL;DR: A new approach to do SAR ATR, in which a multiview deep learning framework was employed, which is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation.
Dissertation
Face recognition based on image sets
TL;DR: A generalized subspace distance (GSD) framework is proposed to illustrate the underlying relationships among the existing methods, which can be considered as special cases of the proposed framework in view of the unsupervised face recognition systems.
Journal ArticleDOI
Multimodal Task-Driven Dictionary Learning for Image Classification
TL;DR: This paper proposes a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information and presents an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level.
Journal ArticleDOI
Multi-view low-rank dictionary learning for image classification
TL;DR: This paper provides a multi-view dictionary low-rank regularization term to solve the noise problem, and designs a structural incoherence constraint for multi-View DL, such that redundancy among dictionaries of different views can be reduced.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.