An Efficient Deep Learning Framework for Face Mask Detection in Complex Scenes

Khan, Sultan Daud; Ullah, Rafi; Rahim, Mussadiq Abdul; Rashid, Muhammad; Ali, Zulfiqar; Ullah, Mohib; Ullah, Habib

doi:10.1007/978-3-031-08333-4_13

Cited by 3 publications

(1 citation statement)

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“…Moreover, deep learning models have been applied to tasks like image generation, style transfer, image superresolution, and image captioning, highlighting their ability not only to recognize visual content but also to generate and manipulate it. The progress in image recognition can be attributed to various factors, including the availability of extensive labeled datasets, advancements in computing power (such as GPUs and distributed computing), and the continuous exploration and innovation of network architectures and training techniques (Guo et al, 2016;Khan et al, 2020aKhan et al, , 2022Li, 2022;Mathew et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Detection of Facial Mask Using Deep Learning Classification Algorithm

Putri¹,

Athoillah²

2023

JDSIS

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Machine Learning is increasingly popular among the public because of the many devices that are made using the capabilities of Machine Learning algorithms. Machine Learning's ability to process and analyze data quickly and accurately, as well as generate useful and relevant information for users, is the main reason for its popularity. Many machine learning algorithms have been used to solve various problems in society, including Deep Learning (DL). Deep Learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in Deep Learning means that Deep Learning begins layers of sequential representation. This study aims to provide a reference on how to create a system and analyze the results of identifying face masks using deep learning algorithms. From the results of research conducted, it is known that this model can recognize faces well, both those who wear masks and those who do not use masks. This is evident from the average specificity and precision of 96.00%, and the average sensitivity or recall value of 93.47%. In addition, this model has also proven to be quite accurate in conducting overall classification with an average accuracy of 94.73%.

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