R2-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction

The purpose of facial beauty prediction (FBP) is to develop a machine that automatically evaluates facial attractiveness in a human perceptual manner. One of the essential problem of facial beauty prediction is the discriminative facial representation of the prediction model. Previous methods formulate FBP as a specific supervised learning of classification, regression, or ranking. We find that the relative ranking information is useful to improve the regression model of FBP. Based on this observation, this paper proposes a regression model guided by the relative ranking with the state-of-the-art Res NeXt structure to achieve FBP, and we call the model as R
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-ResNeXt. The R
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-ResNeXt facilitates to learn the representation and predictor guided by relative ranking for facial attractiveness assessment in an end-to-end manner. To train the R
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-ResNeXt, we develop an aggregated loss that combines regression loss and pairwise ranking loss linearly. We also design a method to construct a dataset containing relatively -labelled image pairs whose individual images are sampled from the SCUT-FBP benchmark database. The experimental results on the SCUT-FBP benchmark show that our R
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-ResNeXt achieves the state-of-the-art performance compared with related literatures, and further indicates the effectiveness of the deep residual architecture and relative beauty ranking into regression task for facial beauty prediction.