Journal ArticleDOI
A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition
TLDR
A two-phase test sample representation method for face recognition using the representation ability of each training sample to determine M “nearest neighbors” for the test sample and uses the representation result to perform classification.Abstract:
In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M “nearest neighbors” for the test sample. The second phase represents the test sample as a linear combination of the determined M nearest neighbors and uses the representation result to perform classification. We propose this method with the following assumption: the test sample and its some neighbors are probably from the same class. Thus, we use the first phase to detect the training samples that are far from the test sample and assume that these samples have no effects on the ultimate classification decision. This is helpful to accurately classify the test sample. We will also show the probability explanation of the proposed method. A number of face recognition experiments show that our method performs very well.read more
Citations
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Journal ArticleDOI
A Survey of Sparse Representation: Algorithms and Applications
TL;DR: A comprehensive overview of sparse representation is provided and an experimentally comparative study of these sparse representation algorithms was presented, which could sufficiently reveal the potential nature of the sparse representation theory.
Journal ArticleDOI
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
TL;DR: This paper addresses the problem of unsupervised domain transfer learning in which no labels are available in the target domain by the inexact augmented Lagrange multiplier method and can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise.
Journal ArticleDOI
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking
TL;DR: Wang et al. as discussed by the authors proposed a new discriminative correlation filter (DCF) based tracking method, which enables joint spatial-temporal filter learning in a lower dimensional discriminativity manifold, and applied structured spatial sparsity constraints to multi-channel filters.
Proceedings ArticleDOI
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach
TL;DR: The proposed method leverages Fully Convolutional Network (FCN) to generate fix-sized spatial feature maps such that pixel-level features are consistent and can decrease the similarity of coupled images from different persons and increase that from the same person.
Journal ArticleDOI
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking.
TL;DR: Wang et al. as mentioned in this paper proposed a new discriminative correlation filter (DCF) based tracking method with adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning.
References
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Robust Face Recognition via Sparse Representation
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An introduction to kernel-based learning algorithms
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Journal ArticleDOI
Two-dimensional PCA: a new approach to appearance-based face representation and recognition
TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.