Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
This addresses the problem of recognizing faces from low-quality images, which is incremental as it builds on existing deep learning and super-resolution methods.
The paper tackles low-resolution face recognition by proposing a two-branch deep convolutional neural network architecture that maps high- and low-resolution images into a common space, achieving an 11.4% improvement in recognition accuracy for very low-resolution images on the FERET dataset.
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.