Age Detection with Face Mask Using Deep Learning and FaceMaskNet-9

Golwalkar, Rucha; Mehendale, Ninad

doi:10.2139/ssrn.3733784

Cited by 3 publications

(2 citation statements)

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“…Many techniques and algorithms have, therefore, been proposed in the last few years to enable such critical applications also during COVID-19, but with minimal performance loss. CVHA techniques from the group of facilitating algorithms reviewed in this paper include face recognition solutions for masked faces [33,32,34,35,36,37], as well age estimation [38,39] and facial expression recognition approaches [40,41] that were all extended recently with the goal of improving robustness with masked faces.…”

Section: Supporting Solutionsmentioning

confidence: 99%

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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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Section: Supporting Solutionsmentioning

confidence: 99%

“…Golwalker et al [39] conjectured that using large prediction models in age estimation with occluded faces makes it challenging due to the lack of suitable large-scale datasets. When wearing masks, the most discriminative features for age estimation are largely hidden below the masks, like wrinkles on the cheeks and mouth.…”

Section: Masked Face Age Classificationmentioning

confidence: 99%

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

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Age and Gender (Face) Recognition: A Brief Survey

Mascio

Fantozzi

Laura

et al. 2021

Methodologies and Intelligent Systems for Technology Enhanced Learning, 11th International Conference

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A Multi-task Deep Learning System for Face Detection and Age Group Classification for Masked Faces

Yolcu

Öztel

2021

Sakarya University Journal of Science

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COVID-19 is an ongoing pandemic, and according to experts, using a face mask can reduce the spread of the disease. On the other hand, masks cause occlusion in faces and can create safety problems such as the recognition of the face and the estimation of its age. To prevent the spread of COVID-19, some countries have restrictions according to age groups. Also, in different countries, people in some age groups have safety restrictions such as driving and consuming alcohol, etc. But these rules are difficult to follow due to occlusion in faces. Automated systems can assist in monitoring these rules. In this study, a deep learning-based automated multi-task face detection and age group classification system is proposed for masked faces. The system first detects masked/no-masked-faces. Then, it classifies them according to age-groups. It works for multi-person regardless of indoor/outdoor environment. The system achieved 92.0% average precision score for masked face detection using YOLO with resnet50 network. Also, 83.87% accuracy for classifying age groups with masked faces and 84.48% accuracy for nomasked faces using densenet201 network have been observed. It produced better results compared to the literature. The results are significant because they show that a reliable age classification for masked faces is possible.

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Age Detection with Face Mask Using Deep Learning and FaceMaskNet-9

Cited by 3 publications

References 0 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

Age and Gender (Face) Recognition: A Brief Survey

A Multi-task Deep Learning System for Face Detection and Age Group Classification for Masked Faces

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