Mobile Based Attendance System: Face Recognition and Location Detection using Machine Learning

Alburaiki, Mubarak Salem Mubarak; Johar, Gapar Md; Helmi, Rabab Alayham Abbas; Alkawaz, Mohammed Hazim

doi:10.1109/icsgrc53186.2021.9515221

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…Face recognition approaches are generally classi ed as feature-based and holistic approaches. In holistic based approaches, recognition is done based on global features from faces, whereas in featurebased approaches, faces are recognized using local features from faces [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another mobile application that allows lecturers to generate class attendance and students to submit the attendance by scanning their faces using their mobile phone camera along with their location [16]. This application makes use of Azure Face API that has pre-loaded face detection and recognition algorithm and Admin-provided approval process for students that minimizes the effort on the admin of the system by allowing the students to register their faces.…”

Section: Dhirendra Mishramentioning

confidence: 99%

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A Systematic Review of Attendance Systems Based on Face Recognition

Modak

Shah

Surana

et al. 2022

PIJR

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In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the most implemented biometrics across various different fields. Classroom attendance check is a contributing factor to student participation and the final success in the courses. Every institute follows their own way for taking attendance.Some are taking attendance manually using papers or a register file or different biometric techniques. Taking attendance by calling out names or passing around an attendance sheet is time-consuming, and the latter is open to easy fraud.In this paper, the comparative analysis of various existing approaches on attendance management system based on facial recognition that are used to monitor attendance in various institutions using Fingerprint,GPS,RFID etc.is discussed with their limitations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Dhirendra Mishramentioning

confidence: 99%

A Systematic Review of Attendance Systems Based on Face Recognition

Modak

Shah

Surana

et al. 2022

PIJR

View full text Add to dashboard Cite

show abstract

“…Colleges presently use various ways to record student attendance, most of which could be more effective in accuracy and security. Handling many students makes it difficult for an administrator to supervise student data submission for a different method of tracking attendance (Alburaiki et al 2021). Human faces play a significant role in our daily lives, particularly when recognizing persons.…”

Section: Introductionmentioning

confidence: 99%

“…It tracks students' occurrences by identifying their faces from a picture of everyone seated in the classroom and comparing them to a trained set. The study Alburaiki et al (2021) created a mobile application that will enable instructors to develop class attendance reports and students to submit them by taking a photo of their faces with their phone's camera and entering their location. The study Pawaskar and Chavan (2020) focused on the execution of an automated attendance system that records class attendance and manages the class database using facial recognition techniques.…”

Section: Introductionmentioning

confidence: 99%

Next-generation attendance tracking: Automated face detection for lecture attendance

Gupta,

Jain,

Bhat

2023

Multidiscip. Sci. J.

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Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.

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“…Mobile-based face recognition technology is a rapidly developing field with significant implications for security, law enforcement, and mobile applications (Arisandi et al, 2018;Samet & Tanriverdi, 2017). Researchers are currently investigating the accuracy and performance of mobile-based face recognition systems compared to traditional desktop-based systems (Abuzar et al, 2020;Alburaiki et al, 2021;Rodavia et al, 2017). There is growing concern about the privacy implications of mobile-based face recognition and how it can be used to identify individuals without their consent or knowledge (Ahmed Khan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods

Achmad,

AZ,

Solichin

2023

J. Ris. Inform.

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Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. Based on the test results, the method can provide accurate and fast facial recognition results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.

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Mobile Based Attendance System: Face Recognition and Location Detection using Machine Learning

Cited by 14 publications

References 11 publications

A Systematic Review of Attendance Systems Based on Face Recognition

A Systematic Review of Attendance Systems Based on Face Recognition

Next-generation attendance tracking: Automated face detection for lecture attendance

Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods

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