Fast matching pursuit for sparse representation‐based face recognition

Melek, M.; Khattab, Ahmed; Abu-Elyazeed, Mohamed F.

doi:10.1049/iet-ipr.2017.1263

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…The least number of features can give high results and faster execution hence authors have used supervised locality preserving projections (SLPP) to reduce the dimension of features and this method used fast matching pursuit (FMP) method to represent a probe sample which is faster than homotomy algorithm which is used in other SRC based methods. The authors have combined all the advantages and proposed Gabor-SLPP-FMP (GSF) framework [61] which is efficient than conventional SRC methods. This method does not solve undersampled, pose issues of FR.…”

Section: Non-linear Variation For Face Imagesmentioning

confidence: 99%

Sparse representation for face recognition: A review paper

Madarkar

Sharma

Singh

2021

IET Image Processing

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With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state-ofthe-art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non-linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Non-linear Variation For Face Imagesmentioning

confidence: 99%

Sparse representation for face recognition: A review paper

Madarkar

Sharma

Singh

2021

IET Image Processing

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“…As the theory of CS found application in many fields, a novel classification technique based on it was developed by Wright et al [8]. This technique was termed sparse representation-based classification and was applied successfully to face recognition [8,23]. Based on SRC, an iris recognition system was developed in [9].…”

Section: Related Workmentioning

confidence: 99%

“…[8]. This technique was termed sparse representation‐based classification and was applied successfully to face recognition [8, 23]. Based on SRC, an iris recognition system was developed in [9].…”

Section: Related Workmentioning

confidence: 99%

Efficient high‐speed framework for sparse representation‐based iris recognition

2021

Self Cite

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While various frameworks for iris recognition have been proposed, most lack efficiency and high speed. A new framework for iris recognition is presented that is both efficient and fast. Feature extraction is performed by extracting Gabor features and then applying supervised locality‐preserving projections with heat kernel weights, which improves the recognition rate in comparison with the results from unsupervised dimensionality reduction techniques such as principal component analysis, locality‐preserving projections, and random projections. Afterwards, a classification is performed using the recently proposed sparse representation‐based classification (SRC). To considerably improve classification performance, SRC is proposed, using a greedy compressed‐sensing recovery algorithm, as opposed to employing the traditional computationally expensive ℓ1 minimisation. The proposed framework achieves a recognition rate of about 99.5% using two iris databases, with a significant improvement in speed over related frameworks.

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“…The goal of expression classification is to judge the similarity between the features of test images and a certain type of expression features in the training set, and select the type with the largest similarity as the output result. The Support Vector Machine (SVM) [41] and sparse representation-based classification [48] are two popular traditional machine learning methods.…”

Section: B Facial Feature Extraction and Recognitionmentioning

confidence: 99%

WGAN-Based Robust Occluded Facial Expression Recognition

et al. 2019

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Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D 1 and D 2. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss l wr , triplet loss l t , and adversarial loss l a. We optimize the discriminator D 1 to distinguish between real and fake by constructing an adversarial loss l a between the generated complementing images, original un-occluded images, and smallscale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss l c into D 2. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods. INDEX TERMS Facial expression recognition, partial occlusion, image complementation, Wasserstein generative adversarial network.

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Fast matching pursuit for sparse representation‐based face recognition

Cited by 10 publications

References 37 publications

Sparse representation for face recognition: A review paper

Sparse representation for face recognition: A review paper

Efficient high‐speed framework for sparse representation‐based iris recognition

WGAN-Based Robust Occluded Facial Expression Recognition

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