# Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture

## TL;DR

This paper introduces a two-branch deep neural network architecture that enhances low resolution face recognition accuracy and reconstructs high resolution images, outperforming existing methods especially at very low resolutions.

## Contribution

It presents a novel two-branch DCNN architecture with integrated super-resolution for improved low resolution face recognition and image reconstruction.

## Key findings

- 11.4% improvement in recognition accuracy for very low resolution images
- Effective reconstruction of high resolution images from low resolution inputs
- Significant performance gains over state-of-the-art methods

## Abstract

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.