Occluded Face Recognition by Identity-Preserving Inpainting

Li, Chenyu; Ge, Shiming; Hua, Yingying; Liu, Haolin; Jin, Xin

doi:10.1007/978-3-030-04946-1_41

Cited by 5 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…mask removal, is discussed more in details in Section 3.9. Similar in-painting approaches have been shown before to be beneficial, to some degree, in enhancing the recognition performance [92] of occluded faces.…”

Section: Enhancing Masked Face Recognitionmentioning

confidence: 58%

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

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Enhancing Masked Face Recognitionmentioning

confidence: 58%

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

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, occlusion's location and size affected the recognition performance of the remaining nonoccluded part. Li et al [16] proposed that identity-preserving generative adversarial networks could be used to inpaint and recognize occluded faces at the same time. They used an inpainting network, a global-local discriminative network, a parsing network, and an identity network among other networks.…”

Section: Comprehensive Theoretical Basismentioning

confidence: 99%

Facial recognition for partially occluded faces

Naser

Ahmad

Samsudin

et al. 2023

IJEECS

View full text Add to dashboard Cite

Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.

show abstract

“…Indeed, for minimizing blending artifacts, the boundary pixels of the source image and the target image were used in the blending process. To inpaint and recognize occluded faces simultaneously, identity preserving generative adversarial networks were suggested by Li et al [41]. They incorporated an inpainting network, a global-local discriminative network, a parsing network, and an identity network.…”

Section: Reconstructed-face Recognitionmentioning

confidence: 99%

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Ouannes¹,

Khalifa²,

Amara³

2021

View full text Add to dashboard Cite

In the recent years, the face recognition task has attracted the attention of researchers due to its efficiency in several domains such as surveillance and access control. Unfortunately, there are multiple challenges that decrease the performance of face recognition. Partial occlusion is the most challenging one since it often causes a great lack of information. The main purpose of this paper is to prove that facial reconstruction improves the results of facial recognition compared to de-occlusion and full-face recognition in the presence of occlusion. Our objective is to achieve occluded-face recognition, de-occluded-face recognition, and reconstructed-face recognition. Regarding face reconstruction, we introduce two different methods based on Laplacian pyramid blending and CycleGANs. In order to validate our work, we perform two different feature extraction techniques: hand-crafted features and learned features exploiting the final layers of a pre-trained deep architecture model. The experimental results on the EURECOM Kinect Face Dataset (EKFD) and the IST-EURECOM Light Field Face Database (IST-EURECOM LFFD) show that the proposed face reconstruction approach, compared with the face de-occlusion and occluded-face recognition ones, clearly improves the face recognition task. Our method boosts the classification performance in comparison with the state-of-the-art methods, achieving 94.66% on EKFD and 72.35% on IST-EURECOM LFFD.

show abstract

Occluded Face Recognition by Identity-Preserving Inpainting

Cited by 5 publications

References 16 publications

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

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

Facial recognition for partially occluded faces

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Contact Info

Product

Resources

About