[Demo paper] exploring attractive faces: General versus personal preferences

In this paper, we propose a novel Personality&Generality Support Vector Regression (PG-SVR) model to train the personality and generality regression attractiveness models from training facial images and their corresponding attractive scores simultaneously, which is completely different from exis...

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Bibliographic Details
Main Authors: Shaobiao Wang, Lu Fang, Juyong Zhang
Format: Conference Proceeding
Language:English
Subjects:
attractiveness enhancement
facial image
Feature extraction
generality
low rank
Matrix decomposition
personality
Real-time systems
sparse
Sparse matrices
Support vector machines
Training
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Summary:In this paper, we propose a novel Personality&Generality Support Vector Regression (PG-SVR) model to train the personality and generality regression attractiveness models from training facial images and their corresponding attractive scores simultaneously, which is completely different from existing method which returns only one general regression model. The trained PG-SVR serves for facial attractiveness enhancement, constructing low-dimensional reasonable solution space, which reflects the "Generality" and "Personality" attractiveness standard respectively. Experiments demonstrate that our PG-SVR enhanced face image space contains satisfactory results for different users and can be explored in real time.
ISSN:1945-7871
1945-788X
DOI:10.1109/ICMEW.2014.6890628