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Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning
Yang-Yu Fan1; Shu Liu1; Bo Li1; Zhe Guo1; Ashok Samal2; Jun Wan3; Stan Z. Li3
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2017
IssueppPages:1-13
AbstractTwo key challenges lie in the facial attractiveness computation research: the lack of discriminative face representations, and the scarcity of sufficient and complete training data. Motivated by recent promising work in face recognition using deep neural networks to learn effective features, the first challenge is expected to be addressed from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. The inspiration to deal with the second challenge comes from the natural representation of the training data, whereby each training face can be associated with a label (score) distribution given by human raters rather than a single label (average score). This paper, therefore, recasts facial attractiveness computation as a label distribution learning problem. Integrating these two ideas, an end-to-end attractiveness learning framework is established. We also perform feature-level fusion by incorporating the lowlevel geometric features to further improve the computational performance. Extensive experiments are conducted on a standard benchmark, the SCUT-FBP dataset, where our approach shows significant advantages over other state-of-the-art work.; Two key challenges lie in the facial attractiveness computation research: the lack of discriminative face representations, and the scarcity of sufficient and complete training data. Motivated by recent promising work in face recognition using deep neural networks to learn effective features, the first challenge is expected to be addressed from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. The inspiration to deal with the second challenge comes from the natural representation of the training data, whereby each training face can be associated with a label (score) distribution given by human raters rather than a single label (average score). This paper, therefore, recasts facial attractiveness computation as a label distribution learning problem. Integrating these two ideas, an end-to-end attractiveness learning framework is established. We also perform feature-level fusion by incorporating the lowlevel geometric features to further improve the computational performance. Extensive experiments are conducted on a standard benchmark, the SCUT-FBP dataset, where our approach shows significant advantages over other state-of-the-art work.
KeywordFacial Attractiveness Computation Deep Residual Network Label Distribution Feature Fusion Scut-fbp
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19773
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.Northwestern Polytechnical University
2.University of Nebraska-Lincoln
3.NLPR, CASIA
Recommended Citation
GB/T 7714
Yang-Yu Fan,Shu Liu,Bo Li,et al. Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2017(pp):1-13.
APA Yang-Yu Fan.,Shu Liu.,Bo Li.,Zhe Guo.,Ashok Samal.,...&Stan Z. Li.(2017).Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning.IEEE TRANSACTIONS ON MULTIMEDIA(pp),1-13.
MLA Yang-Yu Fan,et al."Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning".IEEE TRANSACTIONS ON MULTIMEDIA .pp(2017):1-13.
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