Mask removal : Face inpainting via attributes

Jiang, Yefan; Yang, Fan; Bian, Zhoufeng; Lu, Changsheng; Xia, Siyu

doi:10.1007/s11042-022-12912-1

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…Recently, learning-based techniques such as deep convolutional neural networks (CNNs) and generative adversarial networks (GANs) have been widely used for a variety of image inpainting tasks, such as eliminating objects [7,8], noises [9], texts [10], and masks [11]. Usually, the proposed CNN-based methods are classified into three categories including coarse-to-fine, coarse-andfine, and structural guidance-based methods.…”

Section: Facementioning

confidence: 99%

E2f-Net: Eyes-to-Face Inpainting Via Stylegan Latent Space

Hassanpour,

Jamalbafrani,

Yang

et al. 2023

Preprint

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Section: Facementioning

confidence: 99%

E2f-Net: Eyes-to-Face Inpainting Via Stylegan Latent Space

Hassanpour,

Jamalbafrani,

Yang

et al. 2023

Preprint

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“…[4] obtained realistic results for large-scale free form shape that suffers intensive computation and training instability [10]. Din et al, [6] dealt with this challenge by applying a two-stage Generative Adversarial Network (GAN), including binary segmentation and image restoration that is faster.…”

Section: Gt Outputmentioning

confidence: 99%

“…Many deep learning-based image inpainting methods [2]- [10] have achieved satisfactory performance for removing an unwanted object from the image. Yu et al [2] proposed a contextual attention-based image inpainting model with two generators and two discriminators that get the relevant data from far-off spatial areas for reproducing the missing pixels.…”

Section: Introductionmentioning

confidence: 99%

Masked Face Inpainting Through Residual Attention UNet

Imran¹,

Islam²

2022

Preprint

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Realistic image restoration with high texture areas such as removing face masks is challenging. The state-of-theart deep learning-based methods fail to guarantee high-fidelity, cause training instability due to vanishing gradient problems (e.g., weights are updated slightly in initial layers) and spatial information loss. They also depend on intermediary stage such as segmentation meaning require external mask. This paper proposes a blind mask face inpainting method using residual attention UNet to remove the face mask and restore the face with fine details while minimizing the gap with the ground truth face structure. A residual block feeds info to the next layer and directly into the layers about two hops away to solve the gradient vanishing problem. Besides, the attention unit helps the model focus on the relevant mask region, reducing resources and making the model faster. Extensive experiments on the publicly available CelebA dataset show the feasibility and robustness of our proposed model. Code is available at https://github.

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“…Jiang et al [91] recently addressed the specific issue of in-painting face mask areas without evaluating the effect on face recognition performance. Such aesthetic-driven face in-painting of the mask area, i.e.…”

Section: Enhancing Masked Face Recognitionmentioning

confidence: 99%

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

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The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting facehand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field.Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

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Mask removal : Face inpainting via attributes

Cited by 14 publications

References 35 publications

E2f-Net: Eyes-to-Face Inpainting Via Stylegan Latent Space

E2f-Net: Eyes-to-Face Inpainting Via Stylegan Latent Space

Masked Face Inpainting Through Residual Attention UNet

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

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