Comparison and Fusion of Multiple Types of Features for Image-Based Facial Beauty Prediction

Facial beauty prediction is an emerging research topic that has many potential applications. Existing works adopt features either suggested by putative rules or borrowed from other face analysis tasks, without an optimization procedure. In this paper, we make a comprehensive comparison of different types of features in terms of facial beauty prediction accuracy, including the rule-based features, global features, and local descriptors. Each type of feature is optimized by dimensionality reduction and feature selection. Then, we investigate the optimal fusion strategy of multiple types of features. The results show that the fusion of AAM, LBP, and PCANet features obtains the best performance, which can serve as a competitive baseline for further studies.

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