Visual attention analysis and prediction on human faces

Abstract

Human faces are almost always the focus of visual attention because of the rich semantic information therein. While some visual attention models incorporating face cues indeed perform better in images with faces, yet there is no systematic analysis of the deployment of visual attention on human faces in the context of visual attention modelling, nor is there any specific attention model designed for face images. On faces, many high-level factors have influence on visual attention. To investigate visual attention on human faces, we first construct a Visual Attention database for Faces (VAF database), which is composed of 481 face images along with eye-tracking data of 22 viewers. Statistics of the eye-movement data show that some high-level factors such as face size, facial features and face pose have impact on visual attention. Thus we propose to build visual attention models specifically for face images through combining low-level saliency calculated by traditional saliency models with high-level facial features. Efficiency of the built models is verified on the VAF database. When combined with high-level facial features, most saliency models can achieve better performance.