Face images in the wild undergo larg巳 intra-p巳rsonal variations, such as poses, illuminations, occlusions, and low resolutions, causing great challenges to facerelated applications. This paper addresses this challenge by proposing a new deep learning framework that can recover the canonical view of face images. It dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness. Unlike the existing face reconstruction methods that were either evaluated in controlled 2D environment or employed 3D information, our approach directly learns the transformation from the face images with a complex set of variations to their canonical views. At the training stage, to avoid the costly process of labeling canonical-view images from the training set by hand, we have devised a new measurement to automatically select or synthesize a canonical-view image for each identity. As an application, this face recovery approach is used for face v巳rification. Facial features are learned from the recovered canonical-view face images by using a facial component-based convolutional neural network. Our approach achieves the state-of-the-art performance on the LFW dataset
More recently, Zhu et al. REF learned the transformation from face images under various poses and lighting conditions to a canonical view with a deep convolutional network.
Recover Canonical-View Faces in the Wild with Deep Neural Networks
ArXiv
ArXiv
Computer Science