Automatic Attendance Management System based on Deep One-Shot Learning

Menezes, Angelo G.; C., Joao M. D. da; Llapa, Eduardo; Estombelo-Montesco, Carlos A.

doi:10.1109/iwssip48289.2020.9145230

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…An automatic model was developed to recognise academic confusion in online learning based on facial expressions to improve the learning effect [29]. An automated class attendance assessment system was offered as a solution for student attendance regulation based on facial recognition and deep one-shot learning, and its method was evaluated in a real-world scenario using various situations and image capturing equipment [30]. Another study adopted a facial recognition technology to regulate attendance [31].…”

Section: Face-basedmentioning

confidence: 99%

“…Then, the researchers should annotate the collected video and label the data according to the research subjects to produce their own datasets. [30] CASIA-WebFace database and FaceScrub database Secondary These two large labelled face recognition datasets can be used for training a face recognition model on over 500K images by combining them.…”

Section: Primarymentioning

confidence: 99%

“…Therefore, the accuracy of the adopted database is crucial for the effective recognition of affective states [22]. In the real world, automated classroom attendance evaluation was demonstrated utilising face recognition and ML techniques [30][55].…”

Section: Online Educationmentioning

confidence: 99%

“…These tasks can be achieved by automated systems or TA robots. For instance, assessing student attendance is critical within the classroom setting, although this task is a tiresome and time-consuming process [30]. In the context of restricted resources, the student attendance evaluation task must be completed automatically using facial recognition technologies [30].…”

Section: Challenges Related To Timementioning

confidence: 99%

“…Various kinds of academic emotions, such as engagement, boredom and frustration, can have a substantial impact on learning; thus, several levels of academic emotion recognition methods can be considered by utilising efficient algorithms [29]. Exploring machine learning methods is recommended to take advantage of the large dataset for training to increase the reliability and robustness of the system [30]. The performance of the TSVM framework can be enhanced further by training this classifier with a large amount of data [87].…”

Section: 421mentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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This study presents a systematic review of the automatic vision-based assessment systems and models from related fields over the last 6 years. Many studies focus on automatic vision-based assessment in the area of robotics, artificial intelligence and virtual reality or augmented reality (AR) to enhance the communication between humans and machines for general purposes. Three reliable databases, IEEEXplore, Science Direct and Web of Science, were used to obtain relevant studies on the given topic. Several stages of filtering and scanning were applied according to the inclusion/exclusion criteria to filter the obtained 3505 papers from 2015 to 2020. Finally, 97 papers met the criteria. They were classified into four main categories based on their field by following the scientific taxonomy: computer-vision-based, robotics-based, AR-based and hybrid-based categories, accounting for 42.26% (n = 41/97 papers), 48.45% (n = 47/97 papers), 6.18% (n = 6/97 papers) and 4.12% (n = 4/97 papers), respectively. Subsequently, a deep and critical analysis of this multifield systematic review highlighted new research opportunities, motivations, challenges and recommendations that need attention to integrate interdisciplinary studies. Thus, automatic vision-based assessment, which is a field requiring automated solutions, tools and methods, enhances the ability of the assistive technology and facilitates the interaction of individuals with machines. Many studies have been conducted on the automatic vision-based assessment systems and their subtypes to promote accurate communication and performance evaluation in human-machine interaction. This study can provide researchers with useful guides and valuable information for future research. This study also addresses the ambiguity of automatic vison-based assessment models in interdisciplinary fields.

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Section: Face-basedmentioning

confidence: 99%

Section: Primarymentioning

confidence: 99%

Section: Online Educationmentioning

confidence: 99%

Section: Challenges Related To Timementioning

confidence: 99%