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Machine Vision, Image Recognition and Machine Learning

[Facial Attractiveness/Beauty Analysis]

Paper:

Lei Zhang*, David Zhang, Ming-Ming Sun, and Fangmei Chen, Facial beauty analysis based on geometric feature: Toward attractiveness assessment application, Expert Systems with Applications (ESWA), vol. 82, pp. 252-265, 2017. [paper]

Abstract:

Facial beauty analysis has been an emerging subject of multimedia and biometrics. This paper aims at exploring the essence of facial beauty from the viewpoint of geometric characteristic toward an interac- tive attractiveness assessment (IAA) application. As a result, a geometric facial beauty analysis method is proposed from the perspective of machine learning. Due to the troublesome and subjective beauty la- beling, the accurately labeled data scarcity is caused, and result in very few labeled data. Additionally, facial beauty is related to several typical features such as texture, color, etc., which, however, can be eas- ily deformed by make-up . For addressing these issues, a semi-supervised facial beauty analysis framework that is characterized by feeding geometric feature into the intelligent attractiveness assessment system is proposed. For experimental study, we have established a geometric facial beauty (GFB) dataset including Asian male and female faces. Moreover, an existing multi-modal beauty (M 2 B) database including west- ern and eastern female faces is also tested. Experiments demonstrate the effectiveness of the proposed method. Some new perspectives on the essence of beauty and the topic of facial aesthetic are revealed. The impact of this work lies in that it will attract more researchers in related areas for beauty exploration by using intelligent algorithms. Also, the significance lies in that it should well promote the diversity of expert and intelligent systems in addressing such challenging facial aesthetic perception and rating issue.

Approach:

Datasets:

Experiments:

Facial beauty score analysis of male faces (left) and female faces (right): (a) predicted score of attractive faces (blue points), universal faces (red points) and unattrac- tive faces (black points); (b) Statistical results of predicted beauty scores of universal faces. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

References:

[1] Gunes, H. (2011). A survey of perception and computation of human beauty. In Pro- ceedings of the 2011 joint ACM workshop on human gesture and behavior under- standing (pp. 19–24). ACM.

[2] Nguyen, T. V. , Liu, S. , Ni, B. , Tan, J. , Rui, Y. , & Yan, S. (2012). Sense beauty via face, dressing, and/or voice. In Proceedings of the 20th ACM international conference on multimedia (pp. 239–248).

[3] Zhang, D. , Zhao, Q. , & Chen, F. (2011). Quantitative analysis of human facial beauty using geometric features. Pattern Recognition, 44 , 940–950.

[4] Gray, D. , Yu, K. , Xu, W. , & Gong, Y. (2010).Predicting facial beauty without land- marks. In ECCV (pp. 434–447).