A Multi-level Supporting Scheme for Face Recognition under Partial Occlusions and Disguise

Yuk, Jacky S-C.; Wong, Kwan-Yee K.; Chung, Ronald

doi:10.1007/978-3-642-19282-1_55

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…The occlusion detection problem was approached by PCA and improved support vector machines (SVM), while the nonoccluded facial part was identified by block-based weighted local binary patterns (LBP). Paper [11][12][13] also divided image into modules but changed the strategy of division into randomized modules, multilevel modules, and horizontal modules.…”

Section: Introductionmentioning

confidence: 99%

Occluded Face Recognition Based on Double Layers Module Sparsity Difference

Zhao

2014

Advances in Electronics

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Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Occluded Face Recognition Based on Double Layers Module Sparsity Difference

Zhao

2014

Advances in Electronics

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“…To overcome the impact of patch scale, multi-scale patch-based methods were proposed. Yuk et al [31] proposed the Multi-Level Supporting scheme (MLS). First, Fisherface based classifiers are built on multi-scale patches.…”

Section: Related Workmentioning

confidence: 99%

Patch-based face recognition using a hierarchical multi-label matcher

Zhang¹,

Dou²,

Kakadiaris³

2018

Image and Vision Computing

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This paper proposes a hierarchical multi-label matcher for patch-based face recognition. In signature generation, a face image is iteratively divided into multi-level patches. Two different types of patch divisions and signatures are introduced for 2D facial image and texture-lifted image, respectively. The matcher training consists of three steps. First, local classifiers are built to learn the local matching of each patch. Second, the hierarchical relationships defined between local patches are used to learn the global matching of each patch. Three ways are introduced to learn the global matching: majority voting, 1 -regularized weighting, and decision rule. Last, the global matchings of different levels are combined as the final matching. Experimental results on different face recognition tasks demonstrate the effectiveness of the proposed matcher at the cost of gallery generalization. Compared with the UR2D system, the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18% on the UHDB31 dataset and IJB-A dataset, respectively.

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“…A two-layer ensemble is proposed to obtain the final prediction. To overcome the impact of patch scale, Yuk et al [44] proposed a Multi-Level Supporting scheme (MLS) with multi-scale patches. First, Fisherface based classifiers are built on multi-scale patches.…”

Section: Related Workmentioning

confidence: 99%

Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

Zhang

Kakadiaris

2018

2018 International Conference on Biometrics (ICB)

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This paper focuses on improving face recognition performance by a patch-based 1-to-N signature matcher that learns correlations between different facial patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches. The proposed matcher consists of three steps. First, based on the signature, the local matching identity and the corresponding matching score of each patch are computed. Then, a fully associative weight matrix is learned to obtain the global matching identities and scores of all the patches. At last, the 1 -regularized weighting is applied to combine the global matching identity of each patch and obtain a final matching identity. The proposed matcher has been integrated with the UR2D system for evaluation. The experimental results indicate that the proposed matcher achieves better performance than the current UR2D system. The Rank-1 accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and the IJB-A dataset, respectively.

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A Multi-level Supporting Scheme for Face Recognition under Partial Occlusions and Disguise

Cited by 8 publications

References 10 publications

Occluded Face Recognition Based on Double Layers Module Sparsity Difference

Occluded Face Recognition Based on Double Layers Module Sparsity Difference

Patch-based face recognition using a hierarchical multi-label matcher

Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

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