Name
Abstract | ||
|---|---|---|
Computational approaches to investigating face attractiveness have become an emerging topic in facial analysis research. Integrating techniques from image analysis, pattern recognition and machine learning, this subarea aims to explore the nature, components and impacts of facial attractiveness and to develop computational algorithms to analyze the attractiveness of a face. In this paper we develop an attractiveness computation model for both frontal and profile images (2.5D). We focus on the role of geometric ratios in the determination of facial attractivenss. Stepwise regression is used as the feature selection method to select the discriminatory variables from a huge set of data-driven ratios. Decision tree is then used to generate an automated classifier for both frontal and profile computation models. The BJUT-3D Face Database is pre-processed and tested as our experimental dataset. The low statistic errors and high correlation indicate the accuracy of our computation models. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23989-7_57 | INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: IMAGE AND VIDEO DATA ENGINEERING, ISCIDE 2015, PT I |
Keywords | Field | DocType |
Facial attractiveness computation, 2.5D, BJUT-3D, Face ratios, Data-driven | Decision tree,Data-driven,Statistic,Feature selection,Pattern recognition,Computer science,Attractiveness,Correlation,Artificial intelligence,Classifier (linguistics),Computation | Conference |
Volume | ISSN | Citations |
9242 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Liu | 1 | 134 | 18.46 |
| FAN YangYu | 2 | 123 | 22.28 |
| Zhe Guo | 3 | 6 | 0.80 |
| A Samal | 4 | 1033 | 213.54 |