Hierarchical face parsing via deep learning

This paper investigates how to parse (segment) facial components from face images which may be partially occluded. We propose a novel face parser, which recasts segmentation of face components as a cross-modality data transformation problem, i.e., transforming an image patch to a label map. Specifically, a face is represented hierarchically by parts, components, and pixel-wise labels. With this representation, our approach first detects faces at both the part- and component-levels, and then computes the pixelwise label maps (Fig.1). Our part-based and componentbased detectors are generatively trained with the deep belief network (DBN), and are discriminatively tuned by logistic regression. The segmentators transform the detected face components to label maps, which are obtained by learning a highly nonlinear mapping with the deep autoencoder. The proposed hierarchical face parsing is not only robust to partial occlusions but also provide richer information for face analysis and face...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Ping Luo, Xiaogang Wang, Xiaoou Tang
• Keywords: Belief Network, Computer Vision, Cvpr 2012, Label Maps, Transformation Problem