Learning Kernel Extended Dictionary for Face Recognition

Huang, Ke-Kun; Dai, Dao‐Qing; Ren, Chuan-Xian; Lai, Zhao-Rong

doi:10.1109/tnnls.2016.2522431

Cited by 49 publications

(25 citation statements)

References 52 publications

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“…Huang et al [34] proposed a kernel extend dictionary(KED) to solve the problem that KDA cannot suppress occlusion variations well. KED has similarities with KRDD, but there are also essential differences.…”

Section: Related Workmentioning

confidence: 99%

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Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

Ding

2019

Applied Sciences

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In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.

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Section: Related Workmentioning

confidence: 99%

“…Huang et al [34] proposed an occlusion model to solve the problem that KDA cannot suppress occlusion variations well. However, such an occlusion model uses only real disturbance and does not take more simulated disturbance conditions into consideration.…”

Section: Learning the Simulated Disturbance Model In Kernel Spacementioning

confidence: 99%

Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

Ding

2019

Applied Sciences

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“…In this subsection, we propose an optimization algorithm to solve the kernellevel fusing model (8), which alternatively calculates A, β β β and σ σ σ. Specifically, we first fix β β β and σ σ σ to calculate A.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Finally, the cell-level histograms are concatenated to produce a global descriptor vector. For LBP's impressive performance [8], many its variants have been proposed for face recognition [1,9,10].…”

Section: Introductionmentioning

confidence: 99%

Fusing landmark-based features at kernel level for face recognition

Huang

Dai

Ren

et al. 2017

Pattern Recognition

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Because of the dramatic intra-class variations in lighting, expression and pose of face images, no single feature is rich enough to capture all the discriminant information, fusing multiple features is an efficient way to improve performance for face recognition. But most of existing fusing methods use features sampling at fixed gird and manually set too many parameters, thus their performances are limited. In this paper, we first propose an improved landmark-based multi-scale LBP feature to address the dramatic pose and expression variations, which samples features around landmarks instead of fixed grid. Then we propose a novel model which fuses LBP feature and Gabor feature at kernel-level to capture the information of facial texture and facial shape, where the weighted coefficients

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“…But the classifiers work in the transformed space. Recently, sparse representation based classifier (SRC) has been successfully used in pattern classification [27,28], and there are many works to extend SRC [29,30,31,32,33]. SRC first codes a testing sample as a sparse linear representation of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error.…”

Section: Introductionmentioning

confidence: 99%

Regularized coplanar discriminant analysis for dimensionality reduction

Huang

Dai

Ren

2017

Pattern Recognition

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The dimensionality reduction methods based on linear embedding, such as neighborhood preserving embedding (NPE), sparsity preserving projections (SPP) and collaborative representation based projections (CRP), try to preserve a certain kind of linear representation for each sample after projection. However, in the transformed low-dimensional space, the linear relationship between the samples may be changed, which can not make the linear representation-based classifiers, such as sparse representation-based classifier (SRC), to achieve higher recognition accuracy. In this paper, we propose a new linear dimensionality reduction algorithm, called Regularized Coplanar Discriminant Analysis (RCDA) to address this problem. It simultaneously seeks a linear projection matrix and some linear representation coefficients that make the samples from the same class coplanar and the samples from different classes not coplanar. The proposed regularization term

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Learning Kernel Extended Dictionary for Face Recognition

Cited by 49 publications

References 52 publications

Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

Fusing landmark-based features at kernel level for face recognition

Regularized coplanar discriminant analysis for dimensionality reduction

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