۱- Introduction

۲- Previous works

۳-  Proposed method

۴- Experimental evaluation

۵- Discussion and conclusion

References

Abstract

We propose a novel coupled 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, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.

Introduction

In the past few decades, face recognition has shown promising performance in numerous applications and under challenging conditions such as occlusion (Jia & Martinez, 2009), variation in pose, illumination, and expression (Martínez, 2002). While many face recognition systems have been developed for recognizing high quality face images in controlled conditions (Zhao, Chellappa, Phillips, & Rosenfeld, 2003), there are a few studies focused on face recognition in real world applications such as surveillance systems with low resolution faces (Pnevmatikakis & Polymenakos, 2007). One important challenge in these applications is that high resolution (HR) probe images may not be available due to the large distance of the camera from the subject. Here, we focus on addressing the problem of recognizing low resolution probe face images when a gallery of high quality images is available. There are three standard approaches to address this problem. (1) down sampling the gallery images to the resolution of the probe images and then performing the recognition. However, this approach is suboptimal because the additional discriminating information available in the high resolution gallery images is lost.