Frontiers of Data and Computing ›› 2022, Vol. 4 ›› Issue (4): 123-131.

CSTR: 32002.14.jfdc.CN10-1649/TP.2022.04.012

doi: 10.11871/jfdc.issn.2096-742X.2022.04.012

• Technology & Application • Previous Articles     Next Articles

Occluded Face Inpainting Method Based on Generative Adversarial Networks

SHI Xuemei1(),ZHU Keliang1,ZHANG Xiangmin1,ZHANG Shutao2,3,CHEN Liangfeng2,*()   

  1. 1. State Grid Anhui Electric Power Co, Ltd. Construction Company, Hefei, Anhui 230022, China
    2. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230026, China
    3. University of Science and Technology of China, Hefei, Anhui 230031, China
  • Received:2021-07-23 Online:2022-08-20 Published:2022-08-10
  • Contact: CHEN Liangfeng;


[Objective] Face recognition, a basic technology in artificial intelligence applications, would be invalid due to the occlusion phenomenon. To improve the face recognition accuracy in the presence of partial occlusions, we propose a method to generate occluded regions of a face image automatically. [Context] The accuracy is the essential factor in face recognition applications. But its performance drastically decreases, and even to zero, when face images are occluded by objects such as helmets, glasses, and masks. [Methods] Taking image attributes into accounts, such as similarity, validity, and consistency, we present an improved generative adversarial network which is composed of one generator and two discriminators. To improve the stability and accuracy of the generator, the occluded part of the face generated by the generator is input to the local discriminator, and together with the original face image without the occluded part serve as the input to the globe discriminator. [Results] The experiments conducted on the LFW dataset show that the Structural Similarity (SSIM) of our method is 67% higher than DCGAN, and the Peak Signal to Noise Ratio (PSNR) is 40% higher. [Conclusions] The results show that prior conditions such as the similarity between pair images can improve the stability and convergence rate of a generator. In future work, a better network architecture that reduces interlayer loss would be proposed to improve the quality of generated images.

Key words: face inpainting, generative adversarial network, image fusion, convolutional neural network, loss function