Recover Canonical-View Faces in the Wild with Deep Neural Networks
This addresses face verification challenges in unconstrained environments, offering a novel method for canonical-view recovery without manual labeling, though it is incremental in improving existing face reconstruction approaches.
The paper tackles the problem of large intra-personal variations in wild face images by proposing a deep learning framework that recovers canonical-view faces, reducing intra-person variances while preserving inter-person discriminativeness. It achieves state-of-the-art performance on the LFW dataset for face verification.
Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related 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 verification. 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.