Cost-sensitive ordinal regression for fully automatic facial beauty assessment

In this paper, we propose a new cost-sensitive ordinal regression (CSOR) approach for fully automatic facial beauty assessment. While there have been several facial beauty assessment methods in the literature, most of them require an accurate set of manual landmarks and are not fully automatic. In many real-world applications, face images are usually captured in unconstrained environments and hence it is desirable to develop fully automatic facial beauty assessment systems for these practical applications. To this end, we develop a fully automatic facial beauty assessment system that behaves like human beings in assessing the concept of facial beauty. To achieve this goal, we propose a new CSOR method to predict the beauty information of face images captured in unconstrained environments. Our method is motivated by the fact that the relative order information of the beauty label information in a face dataset and the costs (losses) of different mis-assessments are usually different. To make better use of these information, we learn a regression model by uncovering the nonlinear structure and preserving the ordinal information of facial images simultaneously. Experimental results on our dataset are presented to demonstrate the efficacy of the proposed approach.