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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2894272
|2 doi
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|a pubmed25n0976.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Deng, Zhongying
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Mutual Component Convolutional Neural Networks for Heterogeneous Face Recognition
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|c 2019
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a HHeterogeneous face recognition (HFR) aims to identify a person from different facial modalities such as visible and near-infrared images. The main challenges of HFR lie in the large modality discrepancy and insufficient training samples. In this paper, we propose the Mutual Component Convolutional Neural Network (MC-CNN), a modal-invariant deep learning framework, to tackle these two issues simultaneously. Our MCCNN incorporates a generative module, i.e. the Mutual Component Analysis (MCA) [1], into modern deep convolutional neural networks by viewing MCA as a special fully-connected (FC) layer. Based on deep features, this FC layer is designed to extract modal-independent hidden factors, and is updated according to maximum likelihood analytic formulation instead of back propagation which prevents over-fitting from limited data naturally. In addition, we develop an MCA loss to update the network for modal-invariant feature learning. Extensive experiments show that our MC-CNN outperforms several finetuned baseline models significantly. Our methods achieve the state-of-the-art performance on CASIA NIR-VIS 2.0, CUHK NIR-VIS and IIIT-D Sketch dataset
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|a Journal Article
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| 700 |
1 |
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|a Peng, Xiaojiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Zhifeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qiao, Yu
|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 (2019) vom: 23. Jan.
|w (DE-627)NLM09821456X
|x 1941-0042
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|g year:2019
|g day:23
|g month:01
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|u http://dx.doi.org/10.1109/TIP.2019.2894272
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