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|Title:||Wow You are so beautiful today||Authors:||Liu, L.
Multiple tree- structured super-graphs model
|Issue Date:||2013||Citation:||Liu, L.,Xu, H.,Xing, J.,Liu, S.,Zhou, X.,Yan, S. (2013). Wow You are so beautiful today. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference : 3-12. ScholarBank@NUS Repository. https://doi.org/10.1145/2502081.2502126||Abstract:||Beauty e-Experts, a fully automatic system for hairstyle and facial makeup recommendation and synthesis, is devel- oped in this work. Given a user-provided frontal face image with short/bound hair and no/light makeup, the Beauty e-Experts system can not only recommend the most suit- Able hairdo and makeup, but also show the synthetic ef- fects. To obtain enough knowledge for beauty modeling, we build the Beauty e-Experts Database, which contains 1; 505 attractive female photos with a variety of beauty at- Tributes and beauty-related attributes annotated. Based on this Beauty e-Experts Dataset, two problems are consid- ered for the Beauty e-Experts system: what to recommend and how to wear, which describe a similar process of se- lecting hairstyle and cosmetics in our daily life. For the what-to-recommend problem, we propose a multiple tree- structured super-graphs model to explore the complex rela- Tionships among the high-level beauty attributes, mid-level beauty-related attributes and low-level image features, and then based on this model, the most compatible beauty at- Tributes for a given facial image can be efficiently inferred. For the how-to-wear problem, an effective and efficient facial image synthesis module is designed to seamlessly synthesize the recommended hairstyle and makeup into the user facial image. Extensive experimental evaluations and analysis on testing images of various conditions well demonstrate the effectiveness of the proposed system. Copyright © 2013 ACM.||Source Title:||MM 2013 - Proceedings of the 2013 ACM Multimedia Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/72217||ISBN:||9781450324045||DOI:||10.1145/2502081.2502126|
|Appears in Collections:||Staff Publications|
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