A transfer learning approach with convolutional neural network for Face Mask Detection

• URL: http://arxiv.org/abs/2310.18928v1
• Date: Sun, 29 Oct 2023 07:38:33 GMT
• Title: A transfer learning approach with convolutional neural network for Face Mask Detection
• Authors: Abolfazl Younesi, Reza Afrouzian, Yousef Seyfari
• Abstract summary: We propose a mask recognition system based on transfer learning and Inception v3 architecture. In addition to masked and unmasked faces, it can also detect cases of incorrect use of mask.
• Score: 0.30693357740321775
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced an enormous crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detecting facemasks in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including the Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN) This paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked faces, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so, this method has achieved an accuracy of 99.47% and 99.33% in training and test data respectively

Related papers

• Real-Time Mask Detection Based on SSD-MobileNetV2 [2.538209532048867]
  An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. Existing mask detection approaches are resource-intensive and do not achieve a good balance between speed and accuracy. In this paper, we propose a new architecture for mask detection.
  arXiv  Detail & Related papers  (2022-08-29T01:59:22Z)
• A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19 [64.88701052813462]
  Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets.
  arXiv  Detail & Related papers  (2022-01-13T03:28:20Z)
• Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach [0.0]
  During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. We present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification.
  arXiv  Detail & Related papers  (2021-12-30T12:32:33Z)
• Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning [23.062034116854875]
  In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus is to wear a face mask. To mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. We propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network.
  arXiv  Detail & Related papers  (2021-10-01T16:44:06Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• Efficient Masked Face Recognition Method during the COVID-19 Pandemic [4.13365552362244]
  coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden.
  arXiv  Detail & Related papers  (2021-05-07T01:32:37Z)
• TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection [53.98866801690342]
  3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from 3D mask attacks. We propose a pure r transformer (TransR) framework for learning live intrinsicness representation efficiently. Our TransR is lightweight and efficient (with only 547K parameters and 763MOPs) which is promising for mobile-level applications.
  arXiv  Detail & Related papers  (2021-04-15T12:33:13Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• A Computer Vision System to Help Prevent the Transmission of COVID-19 [79.62140902232628]
  The COVID-19 pandemic affects every area of daily life globally. Health organizations advise social distancing, wearing face mask, and avoiding touching face. We developed a deep learning-based computer vision system to help prevent the transmission of COVID-19.
  arXiv  Detail & Related papers  (2021-03-16T00:00:04Z)
• BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices [63.56630165340053]
  Face masks offer an effective solution in healthcare for bi-directional protection against air-borne diseases. CNNs offer an excellent solution for face recognition and classification of correct mask wearing and positioning. CNNs can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus.
  arXiv  Detail & Related papers  (2021-02-06T00:14:06Z)
• Face Mask Detection using Transfer Learning of InceptionV3 [3.6016022712620095]
  The most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask.
  arXiv  Detail & Related papers  (2020-09-17T15:34:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.