中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Super-Identity Convolutional Neural Network for Face Hallucination
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Super-Identity Convolutional Neural Network for Face Hallucination
Kaipeng Zhang; Zhanpeng Zhang; Chia-Wen Cheng; Winston H. Hsu; Yu Qiao; Wei Liu; Tong Zhang
2018
Conference NameProc. European Conference Computer Vision ( ECCV)
Conference Date2018
AbstractFace hallucination is a generative task to super-resolve the facial image with low resolution while human perception of face heav- ily relies on identity information. However, previous face hallucination approaches largely ignore facial identity recovery. This paper proposes Super-Identity Convolutional Neural Network (SICNN) to recover iden- tity information for generating faces closed to the real identity. Specif- ically, we define a super-identity loss to measure the identity difference between a hallucinated face and its corresponding high-resolution face within the hypersphere identity metric space. However, directly using this loss will lead to a Dynamic Domain Divergence problem, which is caused by the large margin between the high-resolution domain and the hallucination domain. To overcome this challenge, we present a domain- integrated training approach by constructing a robust identity metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12×14 faces with an 8× upscaling factor. In addition, SICNN significant- ly improves the recognizability of ultra-low-resolution faces.
Department多媒体
URL查看原文
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/13690
Collection集成所
Recommended Citation
GB/T 7714
Kaipeng Zhang,Zhanpeng Zhang,Chia-Wen Cheng,et al. Super-Identity Convolutional Neural Network for Face Hallucination[C],2018.
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