Fast and robust face recognition via coding residual map learning based adaptive masking

Yang, Meng; Feng, Zhizhao; Shiu, Simon; Zhang, Lei

doi:10.1016/j.patcog.2013.08.003

Cited by 30 publications

(7 citation statements)

References 46 publications

(43 reference statements)

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“…Therefore, the reconstruction error is measured according to correlation entropy [36] to reduce the effects of occluded pixels. Yang et al [37] first used adaptive coding residuals to determine the occluded pixels, and identified the real-world face images by combining the coding residual and class centre. Since occlusion pixels in real-world face images have spatial continuity rather than being isolated from each other, Zhou et al [38] used Markov random field to describe the spatial correlation of the occlusion pixels.…”

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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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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“…Pattern classification aims to find the correct class to which a query sample y belongs, given the training samples X from K classes. In the past decades, many pattern classification algorithms [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] have been proposed. Among different types of pattern classification methods, one major category is the representation based methods [9,10,11,12,13,17].…”

Section: Introductionmentioning

confidence: 99%

Sparse, collaborative, or nonnegative representation: Which helps pattern classification?

Zhang

et al. 2019

Pattern Recognition

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121

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The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the 1 -norm sparsity or the 2 -norm collaborative property that brings the success of SR/CR based classification. In this paper, we investigate the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work. Our analyses reveal that NR can boost the representation power of homogeneous samples while limiting the representation power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR. Our experiments demonstrate that the proposed NR based classifier (NRC) outperforms previous representation based classifiers. With deep features as inputs, it also achieves state-of-the-art performance on various visual classification tasks.

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“…Face recognition has attracted a great deal of attentions during the past several decades. Various approaches have been proposed, including Eigenface [1], Fisherface [2], Independent Component Analysis (ICA) [3], Laplacianfaces [4] and some regression models [5] [6] [7] [8] [9]. They can improve recognition performance to several kinds of variations.…”

Section: Introductionmentioning

confidence: 99%

Discriminative multi-layer illumination-robust feature extraction for face recognition

Dai

Ren

et al. 2017

Pattern Recognition

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Tackling illumination variation is a major problem and it is also an important challenge for practical face recognition systems. Some related methods consider that lighting intensity components mainly lie in large-scale features, and they use a lot of image decomposition techniques to extract the small-scale illuminationinvariant features and remove the large-scale features from original face images. However, it argues that the large-scale features contain a lot useful information which can be further extracted, and the small-scale illumination-invariant features are not robust enough due to they contain some detrimental features (noise, etc.).

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Fast and robust face recognition via coding residual map learning based adaptive masking

Abstract: Robust face recognition (FR) is an active topic in computer vision

Cited by 30 publications

References 46 publications

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

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

Sparse, collaborative, or nonnegative representation: Which helps pattern classification?

Discriminative multi-layer illumination-robust feature extraction for face recognition

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