Design and Implementation of Student Attendance System Based on Face Recognition by Haar-Like Features Methods

Indra, Evta; Yasir, Muhammad; Andrian, Andrian; Sitanggang, Delima; Sihombing, Oloan; Tamba, Saut Parsoran; Sagala, Elviana

doi:10.1109/mecnit48290.2020.9166595

Cited by 16 publications

(4 citation statements)

References 3 publications

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“…Face recognition is a technology that identifies a person and validates it by the comparison of the face features previously stored in a database [24]. The underlying technologies in the face recognition method are based on artificial intelligence and machine learning [45]- [47]. There are two major approaches presented in the literature for face recognition.…”

Section: A Existing Technologies For Attendance Monitoring and The Significance Of Face Recognitionmentioning

confidence: 99%

Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

Anzar¹,

Subheesh

Panthakkan

et al. 2021

IEEE Access

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The exceptional circumstance caused by the COVID-19 pandemic demands substantial modifications in the teaching-learning processes across the globe. Teachers and students are making use of online learning in virtual classrooms as an alternative for face-to-face learning in physical classrooms. However, students' attendance management during virtual learning is a challenging problem. It is quite difficult to identify students' disengagement and even to know whether they are in front of their smart devices or not. In this paper, we introduce the 'Random Interval Attendance Management System' (RIAMS), which is an innovative solution for attendance monitoring issues, students' disengagement, and attendance faking during virtual learning. In RIAMS, we employed a face recognition module built using the Dlib open-source software library. In order to improve the efficiency of the system, we introduced two ancillary modalities -verifying students' responses to CAPTCHAs and UIN (Unique Identification Number) queries. Both the face recognition and ancillary modalities operate at random intervals of time. This distinctive feature of randomness in our design ensures that students' attention and engagement in virtual learning are enhanced. Furthermore, the RIAMS' multimodal architecture and its sub-modalities' adaptive weight system enable teachers to customize their attendance strategy for every course. The output analysis of each of the RIAMS modalities and the combined results emphasize the effectiveness and reliability of our system in the attendance management for virtual learning. The novel RIAMS model has the potential to be extensively deployed for virtual learning in post-COVID settings.

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Section: A Existing Technologies For Attendance Monitoring and The Significance Of Face Recognitionmentioning

confidence: 99%

Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

Anzar¹,

Subheesh

Panthakkan

et al. 2021

IEEE Access

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“…This feature is one of the methods of Viola-Jones [27]. Haar-like features are rectangular features [28], which can be given a specific indication of an image or image [29]. Features like Haar are used to identify objects based on the simple value of a feature, not pixel values contained from the image of the object [30] [31].…”

Section: B Cascade Classifiermentioning

confidence: 99%

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Purwono

Ma’arif

Wulandari

2021

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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The progress of a company is influenced by the excellent performance of its employee. The recruitment process should be done in a correct procedure so that it would not have the potential to harm the company. The improved use of social media can be an aspect to be applied in a recruitment process. LinkedIn is a social media platform that has many users which focuses on the career development aspect. Profile photos are commonly used in social media. In physiognomy, a personality analysis can be carried out based on his/her outward appearance. The profile photo can be an aspect of personality analysis with this knowledge. This research aimed to predict the face shape based on LinkedIn profile photos. A Cascade classifier algorithm with a haar-like feature was used to detect the face area. Dlib library was used to detect face landmarks. K-Means algorithm was used to differentiate the border of hair and facial skin. Indicators of the face shape calculation are the value of face angle, which is the arctangent of the face landmarks matrix; similarity value from the standard deviation calculation between horizontal line 1, 2, and 3; and diameter value which resulted from the standard deviation calculation between horizontal line 2 and vertical line 4. We provide output as face shape from the LinkedIn profile photos. Based on ten profile photo samples, only two predictions were incorrect.

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“…At the end, different standard databases for face detection are also given with their features. In [13], focusing on a face recognition-based attendance system with getting a less false-positive rate using a threshold to confidence. Here used Haar cascade for face detection because of their robustness and LBPH algorithm for face recognition.…”

Section: Introductionmentioning

confidence: 99%

Random Interval Tracking System for Virtual Learning

Aneesa¹,

Meera²

2022

IJERCSE

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With the advent of computer technology, E-Learning has become an increasingly popular learning approach in higher Education institutions. Due to the outbreak of COVID-19 pandemic that demands substantial modification in the teaching-learning process across the globe. However, the student’s attendance management in virtual classroom is quite difficult. In this paper I introduce a smart intelligent virtual classroom, which include ML based face recognition system. Further, it can generate dedicated attendance reports, pinpointing students’ attention during virtual learning at random time intervals. Moreover, the novel random interval tracking system can also prevent the dropping out of participants from the virtual classroom. The distinctive feature of randomness in Random Interval Tracking System (RITS) ensures that student’s attention and engagement in virtual classroom are enhanced.

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Design and Implementation of Student Attendance System Based on Face Recognition by Haar-Like Features Methods

Cited by 16 publications

References 3 publications

Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Random Interval Tracking System for Virtual Learning

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