Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic

Vadlapati, Jayanth; S, Senthil Velan; Varghese, Ewin

doi:10.1109/icccnt51525.2021.9579712

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…[2] Pemerintah memberlakukan New Normal, yaitu upaya menjaga produktivitas masyarakat diitengah pandemi COVID-19 Setelah usainya pelaksanaan Pembatasan Sosial Berskala Besar (PSBB) di Indonesia. [3] Oleh karena itu diibutuhkan kedisiplinan dari setiap masyarakat untuk mengikuti protokol yang ada. Mengingat pentingnya pemakaian masker ini, telah banyak dilakukan penelitian untuk mendeteksi pemakaian masker secara otomatis.…”

Section: Pendahuluanunclassified

Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV

Putra,

Triayudi,

Ningsih

2023

JoSYC

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During the COVID-19 pandemic, masks are one of the main measuring tools in carrying out health protocols, masks are also a top priority when carrying out activities outside the home or office. Because masks are quite effective in filtering out disease particles that allow users not to get infected. Therefore, many places have made masks an important requirement in maintaining health protocols during the COVID-19 pandemic. Previously there was a system that had been created to assist the government in implementing the mandatory wearing of masks, but there were still deficiencies. Therefore the authors created a system to detect mask wearing by updating previous researchers using the convolutional neural network (CNN) algorithm. For making this system the author uses the PYTHON and OPENCV programming languages. which will produce four parts in this detection, namely Mask, No Mask, Covered Mouth Chin and Covered Nose Mouth.

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Section: Pendahuluanunclassified

Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV

Putra,

Triayudi,

Ningsih

2023

JoSYC

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“…In modern times and after the unexpected advent of COVID-19 pandemic, a number of researchers have been working on analyzing its different facets [5] [6]. A lot of interesting results have been reported while analyzing the dietary habits, regional behavior, social connections and others using statistical tools and techniques.…”

Section: Existing Workmentioning

confidence: 99%

A Proposed Quantitative Model for the Computation and Analysis of Waning Immunity for COVID-19 Virus among Human Population

Rubini

Choudhary

2022

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)

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Waning Immunity is an important and relevant concept during these days as the COVID-19 pandemic is expected to become endemic in the coming months. By definition, Waning Immunity is the loss of protective antibodies over time and hence necessitates booster shots at regular intervals of time. This quantitative study is on proposition of a model for computing a newly defined metric called Waning Immunity Index (WII). The model takes into account the three group of people namely, susceptible, infected and recovered individuals from the COVID-19 infections. The required data can be collected from the Kaggle repository that contains information on infections, recovery, vaccination and booster doses given on the human population while considering a geographical location. The proposed model and its implementation have thrown light on the spread, control and effect of COVID-19 virus. Results of the proposed model and the measurement can help health officials to seamlessly plan the duration of booster doses administered on vaccinated population. A sample data has been prepared for testing the model and the application of the proposed metrics. Based on the results, it is found that vulnerability of the Waning Immunity increases steeply at some duration and gradually steadies in time.

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“…The COVID-19 pandemic has been crossing the whole world for several years. According to the data from WHO, this global epidemic has caused over 6.95 million death cases and 7.69 hundred million confirmed cases [1]. To prevent the spread of the epidemic and thus protect their lives for this reason in public places, people must wear masks.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition and Model Analysis Using CNN Based on Self-Collected Information

Zhang

2024

HSET

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Face recognition helps people to determine the features and characteristics of a face, which in turn helps the police to find criminals, reduce the crime rate and ensure the safety of the society. In order to further improve the performance of face recognition, the main objective of this research is to collect a large amount of face information using Convolutional Neural Networks (CNN) to achieve effective recognition. Specifically, first, a code is used in the annotation to define the coordinates of the face. This function converts the classification labels into numbers that can be understood by the model. Second, generate targets in order to parse the annotation file and obtain objects from it. Third, a CNN model is constructed. The features in the data are captured using the adaptive weights of the CNN. In addition, the predictive performance of different models is analysed and compared. In addition, the objective function will transform the face coordinates of the file attached to the boxed list, which helps to acquire the commanded faces in the image file. It is shown through experiments that the proposed model can perform face recognition effectively. The proposed program in this paper can provide valuable thoughts and insights to the field.

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Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic

Cited by 14 publications

References 5 publications

Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV

Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV

A Proposed Quantitative Model for the Computation and Analysis of Waning Immunity for COVID-19 Virus among Human Population

Face Recognition and Model Analysis Using CNN Based on Self-Collected Information

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