TY - CHAP U1 - Konferenzveröffentlichung A1 - Gerlach, Tobias A1 - Danner, Michael A1 - Peng, Le A1 - Kaminickas, Aidas A1 - Fei, Wu A1 - Rätsch, Matthias ED - Farinella, Giovanni ED - Radeva, Petia ED - Braz, Jose T1 - Who loves virtue as much as he loves beauty?: Deep learning based estimator for aesthetics of portraits T2 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 5), 27-29 February 2020, Valletta, Malta N2 - ”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 technolo gies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics. KW - benchmark testing KW - facial databases KW - attractiveness of faces KW - social ethics KW - ELO rating KW - predictive models KW - deep learning KW - extreme-gradient-boosting regressor KW - 3D morphable model Y1 - 2020 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-30623 SN - 2184-4321 SS - 2184-4321 SN - 978-989-758-402-2 SB - 978-989-758-402-2 U6 - https://doi.org/10.5220/0009172905210528 DO - https://doi.org/10.5220/0009172905210528 SP - 521 EP - 528 S1 - 8 PB - SCITEPRESS CY - Setúbal, Portugal ER -