IBAtS - Image Based Attendance System: A Low Cost Solution to Record Student Attendance in a Classroom

Budi, Setia; Karnalim, Oscar; Handoyo, Erico D.; Santoso, Sulaeman; Toba, Hapnes; Nguyen, Thi Thuong Huyen; Malhotra, Vishv

doi:10.1109/ism.2018.00037

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…The studies by Samet and Tanriverdi [29] andBudi et al [6] are the most closely related to this study, since they also make use of smart phones. As mentioned before, the Samet and Tanriverdi [29] study had an accuracy of 84.81% at best for the case of more than threetraining images per student, if measured within what was detected.…”

Section: Comparison With Other Studies/systemsmentioning

confidence: 84%

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Semiautomated Class Attendance Monitoring Using Smartphone Technology

Cronjé

Sanders

2020

JAIT

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Class attendance is important. Class attendance recording is often done using ‘roll-call’ or signing attendance registers. These are time consuming, easy to cheat and it is difficult to draw any information from them. There are other, expensive alternatives to automate attendance recording with varying accuracy. This study experimented with a smart phone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully, while keeping the solution cost-effective. The effect of different class sizes was also investigated. The research was done within a pragmatism philosophy, using a prototype in a field experiment. The algorithms that were used, are: Viola-Jones (HAAR features), Deep Neural Network (DNN) and Histogram of Oriented Gradients (HOG) for detection and Eigenfaces, Fisherfaces and Local Binary Pattern Histogram (LBPH) for recognition. The best combination was Viola-Jones combined with Fisherfaces, with a mean accuracy of 54% for a class of 10 students and 34.5% for a class of 22 students. The best all over performance on a single class photo was 70% (class size 10). As is, this prototype is not accurate enough to use, but with a few adjustments, it may become a cheap, easy-to-implement solution to the attendance recording problem.

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Section: Comparison With Other Studies/systemsmentioning

confidence: 84%

“…Budi et al [6] made use of face detection together with images from a smart phone or tablet. Several images would be taken of the class and then their system would use Viola-Jones to break the images up into individual faces.…”

Section: Related Studiesmentioning

confidence: 99%

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Cronjé

Sanders

2020

JAIT

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“…Besides, Mobile-oriented Cloud Hybrid Architecture used deep learning focused on non-verbal signs such as gestures and facial expressions for the emotion recognition system were researched. Methodology on building face datasets relying on two quantitative and qualitative research on the classifier to work for the student attendance management framework [20]. By detecting faces using a webbased student attendance management system together with XAMPP and MySQL for CNN and k-NN classifier [21].…”

Section: Deep Learning-based Intelligent Classroom Experiencesmentioning

confidence: 99%

Design and Implementation of Intelligent Classroom Framework Through Light-Weight Neural Networks Based on Multimodal Sensor Data Fusion Approach

Murugesan¹,

Chettiar²

2021

RIA

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Intelligent classrooms are becoming famous. Multimodal sensor data fusion technique, where data generated from different sensors are fused to derive at some valuable insights from the classroom settings. In this paper, the proposed framework model tries to enable intelligence in a traditional Classroom Environment by experimenting on modules such as Deep Learning based Face recognition systems, Interactive Smart Mirror Assistant, Indoor Classroom Air quality monitors. Sensor hub (Jedi One) helps to visualize and analyse streaming data in real-time. Based on the proposed framework design, experimentations are carried out. The accuracy achieved in the Face Recognition System of 71% has to be increased with 80-90% by finetuning the parameters. In future, Interactive dashboards can be activated via PowerBI or Excel worksheets. Based on the questionnaire study & responses from the participants on AI & IoT systems inside the classrooms, more than 50% responded positively to support the usage of these technologies in a Classroom Environment. The future classrooms will be (DLeIC) Deep Learning enabled IoT Classrooms to lift the educational space into a new dimension. Incorporating Deep Learning on IoT systems can be a savvy and fruitful path to collaborate with generations to come.

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“…Saravanan and Surender [5] and Islam et al [6] build a mobile-based application for the student attendance and mark management system. Budi et al [7] used an SQL server to count and track the attendance of a student and save a list of attendance in the server database. We based our system on face recognition and detection techniques.…”

Section: Related Workmentioning

confidence: 99%

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

2021

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A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.

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IBAtS - Image Based Attendance System: A Low Cost Solution to Record Student Attendance in a Classroom

Cited by 13 publications

References 30 publications

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Design and Implementation of Intelligent Classroom Framework Through Light-Weight Neural Networks Based on Multimodal Sensor Data Fusion Approach

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

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