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CMOS-GAN: Semi-Supervised Generative Adversarial Model for Cross-Modality Face Image Synthesis

文献类型：期刊论文

作者Yu, Shikang2,3; Han, Hu1,2,3; Shan, Shiguang1,2,3; Chen, Xilin2,3
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:144-158
关键词Cross-modality synthesis semi-supervised synthesis cross-modality face recognition generative adversarial networks
ISSN号1057-7149
DOI10.1109/TIP.2022.3226413
英文摘要Cross-modality face image synthesis such as sketch-to-photo, NIR-to-RGB, and RGB-to-depth has wide applications in face recognition, face animation, and digital entertainment. Conventional cross-modality synthesis methods usually require paired training data, i.e., each subject has images of both modalities. However, paired data can be difficult to acquire, while unpaired data commonly exist. In this paper, we propose a novel semi-supervised cross-modality synthesis method (namely CMOS-GAN), which can leverage both paired and unpaired face images to learn a robust cross-modality synthesis model. Specifically, CMOS-GAN uses a generator of encoder-decoder architecture for new modality synthesis. We leverage pixel-wise loss, adversarial loss, classification loss, and face feature loss to exploit the information from both paired multi-modality face images and unpaired face images for model learning. In addition, since we expect the synthetic new modality can also be helpful for improving face recognition accuracy, we further use a modified triplet loss to retain the discriminative features of the subject in the synthetic modality. Experiments on three cross-modality face synthesis tasks (NIR-to-VIS, RGB-to-depth, and sketch-to-photo) show the effectiveness of the proposed approach compared with the state-of-the-art. In addition, we also collect a large-scale RGB-D dataset (VIPL-MumoFace-3K) for the RGB-to-depth synthesis task. We plan to open-source our code and VIPL-MumoFace-3K dataset to the community (https://github.com/skgyu/CMOS-GAN).
资助项目National Key Research and Development Program of China[2017YFA0700804] ; National Natural Science Foundation of China[61732004] ; National Natural Science Foundation of China[62176249]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000902111900011
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/20151]  
专题中国科学院计算技术研究所期刊论文
通讯作者Han, Hu
作者单位1.Peng Cheng Lab, Shenzhen 518055, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Shikang,Han, Hu,Shan, Shiguang,et al. CMOS-GAN: Semi-Supervised Generative Adversarial Model for Cross-Modality Face Image Synthesis[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:144-158.
APA Yu, Shikang,Han, Hu,Shan, Shiguang,&Chen, Xilin.(2023).CMOS-GAN: Semi-Supervised Generative Adversarial Model for Cross-Modality Face Image Synthesis.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,144-158.
MLA Yu, Shikang,et al."CMOS-GAN: Semi-Supervised Generative Adversarial Model for Cross-Modality Face Image Synthesis".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):144-158.

入库方式： OAI收割

来源：计算技术研究所

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