Abstract
One of the biggest challenges in the problem of facial attractiveness prediction is the lack of reliable labeled training data. It is very hard to apply a well defined concept to describe the attractiveness of a face. In fact, facial attractiveness prediction is a label ambiguity problem. In order to solve the problem, we propose a novel deep architecture called Deep Adaptive Label Distribution Learning (DALDL). Different from previous works, we use discrete label distribution of possible ratings rather than single label to supervise the learning process of facial attractiveness prediction, and update the label distribution automatically during training process. Our approach provides a better description for facial attractiveness, and experiments have shown that DALDL achieves better or comparable results than the state-of-the-art methods.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61871052 and 61573068.
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Chen, L., Deng, W. (2019). Facial Attractiveness Prediction by Deep Adaptive Label Distribution Learning. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_22
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DOI: https://doi.org/10.1007/978-3-030-31456-9_22
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