Face Mask Detection &amp; Attendance System

Facial recognition has become a growing topic among Computer Vision researchers because it can solve real-life problems, including during the COVID-19 pandemic. The pandemic is why the Indonesian government has imposed social restrictions and physical contact in public places. Before the pandemic, most touch-based attendance systems used fingerprints or Radio Frequency Identification (RFID) cards. The solution proposed in this study is to identify real-time facial recognition of the Civil Service presence system using a Deep Neural Network. The goal is to minimize physical contact. The research stages include data collection, augmentation and preprocessing, CNN modeling and training, model evaluation, converting to OpenCV DNN, implementation of transfer learning, and identification of test data. This research contributes to testing variations in distance and position so it can recognize a person's face even when wearing a mask and glasses. This DNN model produces a validation accuracy value of 99.48% and a validation loss of 0.0273 with a data training process of 10 times. Tests for variations in distance, position, use of masks, and glasses on MTCNN detection provide an average accuracy for each trial of 100%, 96%, and 100%, respectively. Therefore, the average accuracy of the Haar Cascades detection test is 100%, 85%, and 100%.

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“…The result of the image convolution process is called a feature map. The calculation of the convolution operation is shown in equation (1).…”

Section: Convolutional Neural Network (Cnn) Modeling and Trainingmentioning

confidence: 99%

“…Computer Vision is a part of Computer Intelligence that instructs data processing machines to study and interpret the visual world [1] . Facial recognition has become one of the most popular and growing topics in Computer Vision over the last few decades.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Face Recognition Civil Servant Presence System Using DNN Algorithm

Putra

Imelda

2022

Indonesian J. Comput. Cybern. Syst.

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“…Restrictions extended to both large gatherings and smaller assemblies. Work-from-home and remote learning became commonplace, with students worldwide transitioning to online classes [8][9][10][11]. As https://doi.org/10.56578/jii010301 infection rates declined, restrictions were gradually relaxed.…”

Section: Introductionmentioning

confidence: 99%

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Haq,

Saqlain

2023

JII

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Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.

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Face Mask Detection & Attendance System

Cited by 2 publications

References 0 publications

Real-Time Face Recognition Civil Servant Presence System Using DNN Algorithm

Real-Time Face Recognition Civil Servant Presence System Using DNN Algorithm

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

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