Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

Talahua, Jonathan S.; Buele, Jorge; Calvopiña, Paola; Varela-Aldás, José

doi:10.3390/su13126900

Cited by 58 publications

(26 citation statements)

References 56 publications

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“…Multi-stage methods always consist of multiple processing steps [32], [35], [40], [128]- [131]. For example, human detection or face region detection, ROI extraction or feature vector extraction, normalization, classification or prediction by sequences and so on.…”

Section: Multi-stage Methodsmentioning

confidence: 99%

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang¹,

Chen²,

Zheng³

2022

Preprint

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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 such as safety monitoring, disease diagnosis, infection risk assessment, lesion segmentation of COVID-19 CT scans, etc. The coronavirus epidemics have forced people wear masks to counteract the transmission of virus, which also brings difficulties to monitor large groups of people wearing masks. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets. We survey the recent advances, beginning with the descriptions of masked facial detection datasets. Thirteen available datasets are described and discussed in details. Then, the methods are roughly categorized into two classes: conventional methods and neural network-based methods. Conventional methods are usually trained by boosting algorithms with handcrafted features, which accounts for a small proportion. Neural network-based methods are further classified as three parts according to the number of processing stages. Representative algorithms are described in detail, coupled with some typical techniques that are described briefly. Finally, we summarize the recent benchmarking results, give the discussions on the limitations of datasets and methods, and expand future research directions. To our knowledge, this is the first survey about masked facial detection methods and datasets. Hopefully our survey could provide some help to fight against epidemics. <br>

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Section: Multi-stage Methodsmentioning

confidence: 99%

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang¹,

Chen²,

Zheng³

2022

Preprint

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“…Several works have tried to address this problem. Talahua et al proposed a facial recognition system that could recognize a person even when using a face mask [65]. The system utilized OpenCv for facial detection, MobileNetV2 handles the face mask-wearing recognition.…”

Section: Kamencay Et Al Compared the Of Cnn With Principal Component ...mentioning

confidence: 99%

A Survey of Face Recognition based on Convolutional Neural Network

Saragih

2022

Indonesian J. of Inf. Syst.

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Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.

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“…In intelligent waste segregation scheme, a system to look after the levels of the container is critical, since it allows authorities to more easily manage the entire conditions of all dustbins. A cloud-based trash management system [10][11] was presented in this paper. Due to the longer range supplied by ultrasonic sensors compared to IR sensors, ultrasonic sensors are utilized to intelligence the status of garbage in the dustbin in their experiment.…”

Section: Related Workmentioning

confidence: 99%

Accurate and High Speed Garbage Detection and Collection Technique using Neural Network and Machine Learning

Usha

Mahesh

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Due to the increasing development of cities’ populations, which has resulted in massive garbage output, waste management systems in urban areas are confronting issues. The ravage of possessions can be employed powerfully with the incorporation of the internet of things (IoT), TensorFlow based deep learning model, as conventional ravage managing system are extremely uneconomical. The major goal of this study is to create a smart waste management system based on a deep learning model that optimizes trash isolation and allows for bin status monitoring in an IoT context. Yolo real time object detection algorithm is employed and educated with a dataset that includes paper, cardboard, glass, metal, and plastic for garbage sorting and grouping. Yolo algorithm enhances the detection speed and yields precise findings with low background noise. Yolo uses convolutional neural network to detect the object. The camera module detects garbage and the servomotor linked to a plastic board, categorizes the waste into the appropriate waste cubicle using the educated model on TensorFlow Lite and Raspberry Pi 4. The garbage fill is monitored by an ultrasonic sensor, and the latitude and longitude are obtained in real time by a GPS module. The smart bin’s LoRa module transmits the bin’s status to the LoRa receiver at 915 MHz. The smart bin’s electronic mechanisms are safeguarded by an RFID-based locker that can only be opened with a registered RFID badge for maintenance or upgrades. This work is framed out of the technologies such as Robotics, neural network, Internet of Things and deep learning algorithm. The garbage detection system is more precise and faster than the other existing methods. The YOLO algorithm can predict objects in real time, which speeds up detection. It’s a prediction method that produces exact results with little background noise. The algorithm has outstanding learning capabilities, allowing it to learn and apply object representations to object detection.

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Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

Cited by 58 publications

References 56 publications

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

A Survey of Face Recognition based on Convolutional Neural Network

Accurate and High Speed Garbage Detection and Collection Technique using Neural Network and Machine Learning

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