Learning guided convolutional neural networks for cross-resolution face recognition

Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task accordingly. In this paper, we propose Guided Convolutional Neural Network (Guided-CNN), which is a novel CNN architecture with parallel sub-CNN models as guide and learners. Unique loss functions are introduced, which would serve as joint supervision for images within and across resolutions. Our experiments not only verify the use of our model for cross-resolution recognition, but also its applicability of recognizing face images with different degrees of occlusion.