Face images in real Closed-Circuit Television (CCTV) are usually with low resolution, which remarkably deteriorates the performance of existing face recognition algorithms and hinders the application of face recognition. The main technical focus of this issue, matching between high-resolution (HR) and low-resolution (LR) face images has attracted significant attention. In order to better address this problem, we propose a Classifier Shared Deep Network with Multi-Hierarchy Loss (CS-MHL-Net) for low-resolution face recognition (LRFR) in this paper. Firstly, considering that contrastive loss and its variants are not conducive to the convergence of network and the reduction of discrepancy, a shared classifier between HR and LR face images is proposed to further narrow the domain gap between HR and LR by sharing the corresponding weights which can be seen as the class center. Secondly, to fully exploit intermediate features and loss constraints, we embed multi-hierarchy loss into intermediate layers, with the target of reducing the distances between HR and LR intermediate features after max pooling and avoiding the decreasing of accuracy caused by over-utilization of intermediate features. Experimental results on LFW and SCface demonstrate the effectiveness and superiority of the proposed method.
Convolutional neural networks (CNNs) have achieved great success in many fields such as object classification [1, 2], scene understanding [3, 4], and action recognition . Most importantly, CNNs have greatly improved the perfor mance of face recognition [6, 7, 8, 9] in recent years, which laid the foundation for face recognition in real applications. Current accuracy of the-state-ofthe-art face recognition algorithms has achieved more than 99% on the LFW database . However, in reality, the qualities of images captured by surveillance videos are severely affected by different image resolutions. The recognition accuracy dropped severely when identifying extremely low-resolution images. In this paper, we will focus on improving the performance of low-resolution face recognition (LRFR) which has made progress and many more [11, 12, 13, 14, 15, 16, 17, 18]. This paper focuses on the matching problem between low-resolution (LR) face images and high-resolution (HR) face images. How to make the network extracting discriminative features of LR face images and narrowing the domain gap between HR and LR are the main directions to improve the performance of LRFR. There are many traditional works [14, 19, 20, 13, 21, 22, 23, 24, 18, 15, 25, 26] making contributions to the improvement of LRFR. Some of these works [14, 13, 21, 19] focus on transforming the LR images to HR images and promoting the recognition accuracy through the reconstructed LR images. The other works [19, 20, 22, 23, 24, 18, 15, 25, 26] pay more attention to the process of extracting the LR features and narrow the distances between LR features and HR features.