Automated attendance system using machine learning approach

Rathod, Hemantkumar; Ware, Yudhisthir; Sane, Snehal; Raulo, Suresh; Pakhare, Vishal; Rizvi, Imdad

doi:10.1109/icnte.2017.7947889

Cited by 48 publications

(16 citation statements)

References 6 publications

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“…Thanks to its convenience in acquisition and reliable and friendly interaction, human face recognition systems have become an important tool in automatic attendance-taking systems. Rathod et al [27] develop an automated attendance-taking system based on face detection and recognition algorithms. After installing the camera in a classroom, it captures the frames containing the faces of all students sitting in the class.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Pei

Zhang

et al. 2019

Electronics

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Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.

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Section: Related Workmentioning

confidence: 99%

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Pei

Zhang

et al. 2019

Electronics

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“…With regard to the attendance system based on face recognition, the minimum number of templates is five [31] while the maximum value is 1000 [59]. Furthermore, the numbers of templates for other face recognition systems are below sixty templates [57,60,61] with most of them having values in between six and twenty-one [29,33,46,53,[62][63][64][65][66][67][68][69]. Next is the authentication stage that involved matching captured biometric data with those templates in the database.…”

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%

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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“…Furthermore, the framework is trained to classify the sexual orientation of the students present in the class. Hemantkumar Rathod et al [9] proposed an attendance management framework which appraises the participation of every understudy by constant clicking of pictures for quite a while and finding the best-restricted picture for processing. This method is secure, dependable and simple to use.…”

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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Automated attendance system using machine learning approach

Cited by 48 publications

References 6 publications

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

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

Improved Intuitive Automated Attendance System using Unorthodox Algorithms

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