Abstract
It is useful to automatically select the most attractive face images from large photo collections. Previous works in this area have little attention on facial attractiveness for one subject, but different objects. In this paper, we have a collection of subjects’ faces including a range of expression, postures, makeup, lighting and resolutions from Bing Search. Given training data of faces scored based on the majority of subjects’ tastes, we train a model to learn how to rank novel faces and show how it can be used to automatically mine attractive photos from personal photo collections. Our system achieves an average accuracy of 73 % on pairwise comparisons of novel faces.
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Acknowledgments
This work was partially sponsored by supported by the NSFC (National Natural Science Foundation of China) under Grant No. 61375031, No. 61573068, No. 61471048, and No. 61273217, the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01, This work was also supported by Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.
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Liao, Y., Deng, W., Cui, C. (2016). Rank Beauty. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_15
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DOI: https://doi.org/10.1007/978-981-10-3005-5_15
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