Learning efficient facial landmark model for human attractiveness analysis

Abstract

Existing geometric features on facial attractiveness analysis only focus on the ratios and distances, which is incomplete to represent all the information of a face. In this paper, we introduce a new category of feature, i.e., the angle features, to describe the angle of different organs such as the chin and eyes, which help boost the analysis performance in experiment. In addition, existing facial beauty analysis papers usually apply existing landmark models and extract their own different geometric feature sets on the landmarks. On the one hand, the geometric features are quite chaotic between different papers. On the other hand, most of the landmarks in the existing landmark model are useless for geometric feature extraction which wastes a lot of computational resources. To tackle these issues, we suggest to define a common geometric feature set and learn a special landmark model for attractiveness analysis. Specially, we collect all the available geometric features from the previous jobs and introduce a genetic feature selection algorithm to select the most effective geometric features. Furthermore, we introduce a special landmark model which exactly covers all the extracted geometric features. The experiments show that our method with the introduced angle features and the common feature set can outperform state-of-art facial beauty estimation methods with geometric features.