Computational face reader

Abstract

The long-history Chinese anthroposcopy has demonstrated the often satisfying capabilities to tell the characteristics (mostly exaggerated as fortune) of a person by reading his/her face, i.e. understanding the fine-grained facial attributes (e.g. single/double-fold eyelid, position of mole). In this paper, we study the face-reading problem from the computer vision perspective and present a computational face reader to automatically infer the characteristics of a person based on his/her face. For example, it can estimate the attractive and easy-going characteristics of a Chinese person from his/her big eyes according to the Chinese anthroposcopy literature. Specifically, to well estimate these fine-grained facial attributes, we propose a novel deep convolutional network in which a facial region pooling layer (FRP layer) is embedded, called FRP-net. The FRP layer uses the searched facial region windows (locates these facial attributes) instead of the commonly-used sliding windows. The experiments on facial attribute estimation demonstrate the potential of the automatic face reader framework, and qualitative and quantitative evaluations from the attractive and smart perspectives of face reading validate the excellence of the presented face reader framework.