Authors:
Tobias Gerlach
1
;
Michael Danner
2
;
1
;
Le Ping Peng
3
;
1
;
Aidas Kaminickas
2
;
Wu Fei
4
and
Matthias Rätsch
1
Affiliations:
1
ViSiR, Reutlingen University, Reutlingen, Germany
;
2
Centre for Vision, Speech & Signal Processing, University of Surrey, Guildford, U.K.
;
3
Philosophy, Hunan University of Science and Technology, Xiangtan, China
;
4
School of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China
Keyword(s):
Benchmark Testing, Facial Databases, Attractiveness of Faces, Social Ethics, ELO Rating, Predictive Models, Deep Learning, Extreme-Gradient-Boosting Regressor, 3D Morphable Model.
Abstract:
”I have never seen one who loves virtue as much as he loves beauty,” Confucius once said. If beauty is more important as goodness, it becomes clear why people invest so much effort in their first impression. The aesthetic of faces has many aspects and there is a strong correlation to all characteristics of humans, like age and gender. Often, research on aesthetics by social and ethic scientists lacks sufficient labelled data and the support of machine vision tools. In this position paper we propose the Aesthetic-Faces dataset, containing training data which is labelled by Chinese and German annotators. As a combination of three image subsets, the AF-dataset consists of European, Asian and African people. The research communities in machine learning, aesthetics and social ethics can benefit from our dataset and our toolbox. The toolbox provides many functions for machine learning with state-of-the-art CNNs and an Extreme-Gradient-Boosting regressor, but also 3D Morphable Model technol
ogies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics.
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