Attendance monitoring system using facial recognition with audio output and gender classification

Poornima, S.; Sripriya, N.; Vijayalakshmi, B.; Vishnupriya, P

doi:10.1109/icccsp.2017.7944103

Cited by 16 publications

(7 citation statements)

References 3 publications

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“…In addition, it also helps in improving efficiency and reduces the educator's burden as the attendance is marked automatically. Besides marking attendance, some systems can determine the students' seating positions [15,[27][28][29] while the other classifies gender of students using facial features [30]. There are several aspects and specification to be considered in developing this type of system.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…For the iris, voice, and fingerprint, students are aware because they need to be in contact with the devices to capture their biometric traits. However, most of the facebased systems are contactless, and hence, students do not know when will the attendance be taken by the camera [30,31,49,50,57,63,64,68,70,71]. Nevertheless, there are some cases where the students are mindful that their facial images are being captured, because they are required to face the front of the camera [46,65,72].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…Moreover, there were some attendance systems using two cameras with different capturing tasks. For example, the system by Kawaguchi et al using sensing and capturing cameras as described previously [27] separates cameras for enrollment and recognition [49], as well as two nonintersecting cameras for students seating in two columns [30]. In addition, six cameras were installed in the whole class for better resolution [57].…”

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confidence: 99%

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Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Hoo

Ibrahim

2019

Journal of Sensors

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The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Section: Journal Of Sensorsmentioning

confidence: 99%

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confidence: 99%

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Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Hoo

Ibrahim

2019

Journal of Sensors

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“…They used facial fiducial points of the understudy's countenances through which attendance can be registered. S Poornima et al [8] gives a framework that can consequently distinguish the student in the classroom imprints the participation by perceiving their countenances alongside sex characterization and sound yield. This system is developed by capturing ongoing human faces in the class.…”

Section: Related Workmentioning

confidence: 99%

Improved Intuitive Automated Attendance System using Unorthodox Algorithms

Wakchaure¹,

Shaikh²,

Rohida³

et al. 2019

IJCA

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One of the troublesome undertakings in Image Processing is accurately recognizing faces in videos, which consecutively offers less precision. Recognizing faces irrespective of the gender is an effortless task for humans; however, from a machine or a robot's perspective, it is an entangled task. Identifying the gender of an individual using their voice is simple when contrasted with facial pictures. This kind of binary classification can be used in various applications, for example, a surveillance system, directed publicizing, demographic gathering, human-machine interaction, content-based indexing and searching, biometrics, and so forth. In order to detect faces with ranging accuracy, there are multiple methods proposed by researchers, yet no existing system can produce an accuracy of 100%. In this manuscript, an automated attendance system is built, to recognize faces from existing trained dataset of student images collected from live training. Along with Face Recognition, the system is equipped with second-level security of QR code scanning. At the testing stage, the experimental results exhibited a precision of 93.00% and an f1-score of 0.9.

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“…Amena Khatun et al implemented a biometric-based attendance system that can automatically capture students' attendance by recognizing their iris [2]. Poornima et al presented an automated attendance system based on face recognition [3]. Sunil Jadhav et al have used a smart phone application, where in the mobile device acts as a scanner to scan the QR code present on the student's ID card and register the student's attendance [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Biometric Based Attendance Registration and Consolidation System Using Raspberry Pi3 and Amazon Web Server

Sunehra¹

2019

IJARET

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Conventionally, in schools and colleges, attendance is taken by the teacher by calling students names or roll numbers, mark his or hers presence or absence in the attendance register. Later attendance is fed into a computer and the attendance percentage is calculated using software in offline mode. This method is time consuming and also requires more man power. In this project, a portable Biometric based Attendance Registration and Consolidation System (BARCS) using Raspberry Pi3 and Amazon Web Server is developed. This handheld biometric attendance registration device can be carried by the teacher to the classroom to register the student's attendance of a particular session through circulation among students. At the end of a session, attendance data gets stored in a database in the Raspberry Pi computer. Then, Pi computes the subject wise attendance percentage and sends it to the Amazon web server for uploading on the developed webpage. The user can view the subject wise attendance by accessing the webpage. Also an SMS is sent to the students mobile when their attendance gets registered. The developed system is intended for programmes wherein students have to meet the minimum attendance requirements in each subject of the programme in a particular semester/year.

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Attendance monitoring system using facial recognition with audio output and gender classification

Cited by 16 publications

References 3 publications

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Improved Intuitive Automated Attendance System using Unorthodox Algorithms

Biometric Based Attendance Registration and Consolidation System Using Raspberry Pi3 and Amazon Web Server

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