Automatic analysis of facial attractiveness from video

Abstract

There has been a growing interest in the computer science field for automatic analysis and recognition of facial beauty and attractiveness. Most of the proposed studies attempt to model and predict facial attractiveness using a single static facial image. While a static image provides limited information about facial attractiveness, using a video clip that contains information about the motion and the dynamic behaviour of the face provides a richer understanding and valuable insights into analysing facial attractiveness. With this motivation, we propose to use dynamic features obtained from video clips along with static features obtained from static frames for automatic analysis of facial attractiveness. Support Vector Machine (SVM) and Random Forest (RF) are utilised to create and train models of attractiveness using the features extracted. Experimental results show that combining static and dynamic features improve performance over using either of these feature sets alone, and SVM provides the best prediction performance.