Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution

Sensors

Yung-Hui Li¹  Chi-En Huang¹  Muhammad Saqlain Aslam²  Ching-Chun Chang³ 

[1] AI Research Center, Hon Hai Research Institute, Taipei 114699, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan;Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;

关键词: super-resolution;    attention mechanism;    generative adversarial network;    biometric recognition;   

DOI  :  10.3390/s21237817

来源: DOAJ

【 摘 要 】

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.

【 授权许可】

Unknown