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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3132827
|2 doi
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a He, Mingjie
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Locality-Aware Channel-Wise Dropout for Occluded Face Recognition
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 10.01.2022
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|a Date Revised 10.01.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the training images with artificial occlusions has been proved as a useful approach. However, these artificial occlusions are commonly generated by adding a black rectangle or several object templates including sunglasses, scarfs and phones, which cannot well simulate the realistic occlusions. In this paper, based on the argument that the occlusion essentially damages a group of neurons, we propose a novel and elegant occlusion-simulation method via dropping the activations of a group of neurons in some elaborately selected channel. Specifically, we first employ a spatial regularization to encourage each feature channel to respond to local and different face regions. Then, the locality-aware channel-wise dropout (LCD) is designed to simulate occlusions by dropping out a few feature channels. The proposed LCD can encourage its succeeding layers to minimize the intra-class feature variance caused by occlusions, thus leading to improved robustness against occlusion. In addition, we design an auxiliary spatial attention module by learning a channel-wise attention vector to reweight the feature channels, which improves the contributions of non-occluded regions. Extensive experiments on various benchmarks show that the proposed method outperforms state-of-the-art methods with a remarkable improvement
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4 |
|a Journal Article
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| 700 |
1 |
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|a Zhang, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shan, Shiguang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Xiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Zhongqin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xilin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 02., Seite 788-798
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:31
|g year:2022
|g day:02
|g pages:788-798
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TIP.2021.3132827
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