We propose a novel couple mappings method for low resolution face recognition
using deep convolutional neural networks (DCNNs). The proposed architecture
consists of two branches of DCNNs to map the high and low resolution face
images into a common space with nonlinear transformations. The branch
corresponding to transformation of high resolution images consists of 14 layers
and the other branch which maps the low resolution face images to the common
space includes a 5-layer super-resolution network connected to a 14-layer
network. The distance between the features of corresponding high and low
resolution images are backpropagated to train the networks. Our proposed method
is evaluated on FERET data set and compared with state-of-the-art competing
methods. Our extensive experimental results show that the proposed method
significantly improves the recognition performance especially for very low
resolution probe face images (11.4% improvement in recognition accuracy).
Furthermore, it can reconstruct a high resolution image from its corresponding
low resolution probe image which is comparable with state-of-the-art
super-resolution methods in terms of visual quality.

本文通过使用深度卷积神经网络和超分辨网络，提出了一种新的低分辨率人脸识别模型，并在 FERET 数据集上进行了评估，结果显示，相较于其他替代方法，本方法在特别是针对非常低分辨率的探测人脸图像时取得了显著的识别性能提高，并能在保证视觉质量等方面达到与超分辨率方法相当的低分辨率图像重建效果。