Exemplar Guided Cross-Spectral Face Hallucination via Mutual Information Disentanglement | |
Wu, Haoxue1,2; Huang, Huaibo1,2![]() ![]() ![]() ![]() ![]() | |
2021 | |
会议名称 | International Conference on Pattern Recognition |
会议日期 | 2021年1月10日 - 2021年1月15日 |
会议地点 | 意大利米兰 |
摘要 | Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44734 |
专题 | 模式识别实验室 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu, Haoxue,Huang, Huaibo,Yu, Aijing,et al. Exemplar Guided Cross-Spectral Face Hallucination via Mutual Information Disentanglement[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Wu 等。 - 2020 - Exemp(8670KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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