Comparative study of face detection methods in spontaneous videos

Adouani, Amal; Henia, Wiem Mimoun Ben; Lachiri, Zied

doi:10.1109/iccad46983.2019.9037883

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The standard Dlib detector uses histograms of oriented gradients (HOG) features with linear SVMs. Some studies have found that HOG + SVM performs better than Haar cascades or Linear Binary Patterns (LBPs) for face detection in video sequences [10,11]. The multi-task cascaded convolutional neural network (MTCNN) model for face detection [12] consists of three convolutional networks (R-Net, P-Net, and O-Net) and is able to outperform other methods on face detection benchmarks while retaining real-time performance.…”

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confidence: 99%

Two-Stage Facial Mask Detection Model for Indoor Environments

Zereen

Corraya

Dailey

et al. 2020

Advances in Intelligent Systems and Computing

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The COVID-19 pandemic has led to many organizations around the world enforcing face mask rules for personal protection. Manual checking whether individuals entering an organization's premises are wearing masks is cumbersome and possibly confrontational. There has been relatively little work on automatic face mask rule violations thus far. We propose a system for automatic monitoring for face mask rule violations for enterprises. Our method is an efficient two-stage facial mask detection model. The first stage is based on facial landmark extraction and clustering, and the second stage analyzes the clustered nose region. A thorough accuracy evaluation on five types of sample face images (no mask, beard and mustache, single-color mask, multi-color mask, and skin-color mask) finds that the overall accuracy of the two-stage model is an excellent 97.13%, outperforming simpler single-stage models. IntroductionGlobally, at the time of writing, over 36 million people are infected and over 1,056,186 deaths have been caused by the COVID-19 pandemic [1]. The World Health Organization has recommended that mask-wearing can reduce the chances of being infected or spreading COVID-19 by respiratory droplets, which constitute the main vector

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confidence: 99%