Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 7, Pages 4820-4829Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3129629
Keywords
Faces; Face detection; Convolutional neural networks; Face recognition; Mobile handsets; Informatics; Computational modeling; Deep learning; face recognition; lightweight convolutional neural network (CNN)
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Funding
- King Saud University Riyadh, Saudi Arabia, through the Researchers Supporting Project [RSP-2021/18]
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This article proposes a face detection model in risk situations to aid rescue teams, utilizing a lightweight CNN to accurately detect faces in various circumstances. The model is designed for simplicity and mobile device compatibility.
In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.
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