Shuyang Wang
ABSTRACT
Facial attractiveness is an ever-lasting issue in art and social science. It also draws considerable attention from multimedia community recently. In this paper, we develop a framework highlighting attractiveness-aware feature extracted from a pair of auto-encoders to learn human-like assessment of facial beauty. Our work is fully-automatic that does not require any landmark and puts no restrictions on the faces' pose, expressions, and lighting conditions and therefore is applicable on a larger and more diverse dataset. To this end, first, a pair of auto-encoders is built respectively with beauty images and non-beauty images, which can be used to extract attractiveness-aware features by putting test images into both encoders. Second, we further enhance the performance using an efficient robust low-rank fusion framework to integrate the predicted confidence scores which are obtained based on certain kinds of features. We show that our attractiveness-aware model with multiple layers of auto-encoders produces appealing results and performs better than previous appearance-based approaches.
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Index Terms
- Attractive or Not?: Beauty Prediction with Attractiveness-Aware Encoders and Robust Late Fusion
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