Volltext-Downloads (blau) und Frontdoor-Views (grau)

Who loves virtue as much as he loves beauty?: Deep learning based estimator for aesthetics of portraits

  • ”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.

Download full text files

Export metadata

Additional Services

Search Google Scholar


Author of HS ReutlingenGerlach, Tobias; Danner, Michael; Peng, Le; Rätsch, Matthias
Erschienen in: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
Place of publication:Setúbal, Portugal
Editor:Giovanni Farinella, Petia Radeva, Jose Braz
Document Type:Conference proceeding
Publication year:2020
Tag:3D morphable model; ELO rating; attractiveness of faces; benchmark testing; deep learning; extreme-gradient-boosting regressor; facial databases; predictive models; social ethics
Page Number:8
First Page:521
Last Page:528
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International