Intelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method

Nurkhamid,; Setialana, Pradana; Jati, Handaru; Wardani, Ratna; Indrihapsari, Yuniar; Norwawi, Norita Md

doi:10.1088/1742-6596/1737/1/012031

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…The study by Setialana P, Jati H, Wardani R, Indrihapsari Y, Norwawi NM, et al Intelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method. The system achieved an accuracy rate of 99% and reduced the time and effort required for manual attendance management [10].…”

Section: Litrature Reviewmentioning

confidence: 98%

IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking

Verma,

Sharma,

Kumar

et al. 2023

IJRITCC

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Internet of Things based Automatic Attendance Management systems that use Artificial Intelligent cameras and deep learning algorithms can suggestively advance the accuracy and proficiency of class presence following in schools, colleges as well as universities. This technology involves the use of cameras that are placed in classrooms or other areas where attendance needs to be monitored.The cameras are equipped with advanced deep learning algorithms that can detect and recognize students based on their unique facial features. These algorithms use machine learning techniques to analyse images and identify individual faces, even in varying lighting conditions and different angles.The data collected by the cameras is then transmitted to an Intenet of Things based platform, which stores and approach the attendance data in real time. This platform can also be used to generate reports and analytics on attendance, helping teachers and administrators make data driven decisions to improve student performance.

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Section: Litrature Reviewmentioning

confidence: 98%

IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking

Verma,

Sharma,

Kumar

et al. 2023

IJRITCC

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“…The design of a CNN incorporates a multilayer perceptron system optimized to minimize computational demands. Typically, a CNN comprises several layers, including an input layer, an output layer, and a hidden layer composed of multiple convolutional layers, pooling layers, fully connected layers, and normalization layers (Setialana et al, 2021).…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep Transfer Learning Approach for Student Attendance System During the COVID-19 Pandemic

Ennajar,

Bouarifi

2024

Journal of Computer Science

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Marking students' attendance has been a challenge during the COVID-19 pandemic. It is a time-consuming task due to the abnormally high number of students present during a learning session; many studies have been proposed to improve the system. However, there are still issues with each of these systems; we have employed deep transfer learning techniques using six pretrained convolutional neural networks. We created a dataset of faces with masks and we used this dataset to assess six Convolutional Neural Network (CNN) models. We increased the training samples to improve the performance of the pre-trained models. The latter allows us to build a masked face recognition model of learners during a learning session. Due to the COVID-19 pandemic, students don facemasks to safeguard their own well-being and mitigate the spread of the virus. This has created a problem that did not exist before. The experimental findings reveal that pre-trained models, specifically caption and InceptionResNetV2, exhibit outstanding proficiency in precisely identifying faces with masks and require minimal training time.

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“…The overall accuracy obtained by the proposed system in a realtime environment was 95.02%. Similarly, an intelligent face recognizing attendance system that can identify several people simultaneously that is built based on CNN is proposed in [18]. The proposed system was tested with frontal view, side view and downwards view.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh,

Aziz

et al. 2022

IJACSA

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Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it. There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD). The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system. The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset. The results are 87.50%, 92.19% and 95.93% respectively. The overall face recognition accuracy of the proposed system is 98.48%. The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.

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Intelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method

Cited by 15 publications

References 3 publications

IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking

IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking

Deep Transfer Learning Approach for Student Attendance System During the COVID-19 Pandemic

Face Recognition System Design and Implementation using Neural Networks

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