Document Type
Conference Paper
Publication Date
10-2012
Publication Source
Proceedings of the 20th ACM International Conference on Multimedia
Abstract
Discovering the secret of beauty has been the pursuit of artists and philosophers for centuries. Nowadays, the computational model for beauty estimation has been actively explored in computer science community, yet with the focus mainly on facial features. In this work, we perform a comprehensive study of female attractiveness conveyed by single/multiple modalities of cues, i.e., face, dressing, and/or voice, and aim to uncover how different modalities individually and collectively affect the human sense of beauty. To this end, we collect the first Multi-Modality Beauty (M2B) dataset in the world for female attractiveness study, which is thoroughly annotated with attractiveness levels converted from manual k-wise ratings and semantic attributes of different modalities. A novel Dual-supervised Feature-Attribute-Task (DFAT) network is proposed to jointly learn the beauty estimation models of single/multiple modalities as well as the attribute estimation models. The DFAT network differentiates itself by its supervision in both attribute and task layers. Several interesting beauty-sense observations over single/multiple modalities are reported, and the extensive experimental evaluations on the collected M2B dataset well demonstrate the effectiveness of the proposed DFAT network for female attractiveness estimation.
Inclusive pages
239-248
ISBN/ISSN
978-1-4503-1089-5
Document Version
Postprint
Copyright
Copyright © 2012, Association for Computing Machinery
Publisher
Association for Computing Machinery
Place of Publication
Nara, Japan
eCommons Citation
Nguyen, Tam; Liu, Si; Ni, Bingbing; Tan, Jun; Rui, Yong; and Yan, Shuicheng, "Sense Beauty via Face, Dressing, and/or Voice" (2012). Computer Science Faculty Publications. 67.
https://ecommons.udayton.edu/cps_fac_pub/67
Included in
Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Personality and Social Contexts Commons, Social Psychology Commons
Comments
The document provided for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file.