A Deep learning approach to joint face detection and segmentation

Abstract

Robust face detection and facial segmentation are crucial pre-processing steps to support facial recognition, expression analysis, pose estimation, building of 3D facial models, etc. In previous approaches, the process of face detection and facial segmentation are usually implemented as sequential, mostly separated modules. In these methods, face detection algorithms are usually first implemented so that facial regions can be located in given images. Segmentation algorithms are then carried out to find the facial boundaries and other facial features, such as the eyebrows, eyes, nose, mouth, etc. However, both of these tasks are challenging due to numerous variations of face images in the wild, e.g. facial expressions, illumination variations, occlusions, resolution, etc. In this chapter, we present a novel approach to detect human faces and segment facial features from given images simultaneously. Our proposed approach performs accurate facial feature segmentation and demonstrates its effectiveness on images from two challenging face databases, i.e. Multiple Biometric Grand Challenge (MBGC) and Labeled Faces in the Wild (LFW).