Sparse representation classification based linear integration of ℓ<sup>1</sup>-norm and ℓ<sup>2</sup>-norm for robust face recognition

Awedat, Khalfalla; Essa, Almabrok; Asari, Vijayan K.

doi:10.1109/eit.2017.8053403

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Several studies [8,9] showed that a powerful classification along with a simple feature extraction algorithm can result in a desirable function of the system. As can be observed in literature [8,9,13], some classification algorithms are able to directly extract information from input images. Reihanian et al [14] used the composition of thermal and visible images for image recognition.…”

Section: Figure 1 Some Samples Of Thermal and Corresponding Visible mentioning

confidence: 97%

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Face Recognition in Thermal Images based on Sparse Classifier

Shavandi

Afrakoti

2019

IJE

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Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent years, using thermal images has gain more attention among the introduced solutions as an effective and unique solution. This article studies the performance of sparse processing techniques when facing with challenges of face recognition problem in thermal images. Also, the potential of the sparse classifier algorithm to receive information directly from input images without using any feature extraction algorithms was studied. The obtained results indicated that the sparse processing techniques outperform the Eigenface and KNN algorithms in terms of addressing the challenges of thermal images. In this work, USTC NVIN and CBSR NIR face datasets were used for simulation purposes. These datasets include the images with different emotional states (sad, happy, etc.) captured in different light conditions; also the images are captured both with and without wearing glasses. Simulation results have shown that sparse classifier can be an effective algorithm for the face recognition problem in thermal images.

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Section: Figure 1 Some Samples Of Thermal and Corresponding Visible mentioning

confidence: 97%

“…Maximum recognition rate reported by Seal et al [12] was 91.47%. Also, Awedat et al [13] used the sparse representation classification based on integration of 1 and 2 norms. They evaluated the function of their system on two databases of visible images.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition in Thermal Images based on Sparse Classifier

Shavandi

Afrakoti

2019

IJE

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“…It provides a balance of sparsity and grouping tradeoff. Many methods have existed, like those in [22]- [24]. Choosing regularization parameters is based on the trial and error strategy, which may be time-consuming.…”

Section: B Adaptive Selecting Of the Regularization Parametersmentioning

confidence: 99%

Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding

et al. 2019

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In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, this paper presents an effective method that exploits nonlocal sparsity by estimating the sparse code changes, which can be done by adding a nonlocal constraint term to the local constraint one. In addition, for generality, the sparse coding and regularization parameters are adaptively estimated. A comparative study demonstrated that the proposed method has better accuracy rates compared to the existing state-of-the-art methods.

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“…However, the unconstrained natural scenes [6,7] bring many challenges for face recognition. Multi-factor changes in the real-world face images may affect the performance of the face recognition methods, such as resolution [8], lighting [9], pose [10], occlusion [11] and expression [12].…”

Section: Introductionmentioning

confidence: 99%

“…The real-world face images with different occlusion types in the natural scenes are shown in Figure 1. Although images, the occlusion face recognition methods can be roughly divided into two categories: robust feature extraction [18][19][20] and robust classifier [11,21,22].…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition for occluded real‐world images using constrained probabilistic sparse network

et al. 2022

IET Image Processing

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Aiming at the occluded real-world face images across illumination, pose, expression, and resolution variations, a robust face recognition for occluded real-world images using constrained probabilistic sparse network is presented. A constrained probabilistic sparse representation network is constructed to obtain the features of all the training images from a global perspective, and the new network nodes are generated through the random combination of the training images. In the probabilistic sparse representation network, the probabilities of each class of the sparse subspace that the occluded test images individually belong to are defined and calculated. The final classifications of the test images are determined by the joint maximum probability of the network nodes. Meanwhile the second-order gradient constraint is the first introduced in the probabilistic sparse representation network. It is found that the constraint uses the adjacent pixels of the face images to obtain the local texture similarity, and further use the local texture similarity to distinguish the occlusion and non-occlusion parts. Thus the constraint can reduce the influence of the occlusion part on face recognition. Extensive experiments with the 12 existing methods on the five face databases demonstrate that the recognition rate of the proposed method is the best than the non-deep learning methods compared, and the proposed method can obtain nearly the same recognition rate with an advantage of a very less time consumption compared to the state-of-the-art deep learning methods.

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Sparse representation classification based linear integration of ℓ¹-norm and ℓ²-norm for robust face recognition

Cited by 5 publications

References 13 publications

Face Recognition in Thermal Images based on Sparse Classifier

Face Recognition in Thermal Images based on Sparse Classifier

Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding

Robust face recognition for occluded real‐world images using constrained probabilistic sparse network

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Sparse representation classification based linear integration of ℓ1-norm and ℓ2-norm for robust face recognition

Cited by 5 publications

References 13 publications

Face Recognition in Thermal Images based on Sparse Classifier

Face Recognition in Thermal Images based on Sparse Classifier

Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding

Robust face recognition for occluded real‐world images using constrained probabilistic sparse network

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Sparse representation classification based linear integration of ℓ¹-norm and ℓ²-norm for robust face recognition