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Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

Pauline Ong, Tze Wei Chong, Woon Kiow Lee
Copyright: © 2020 |Pages: 42
ISBN13: 9781799801825|ISBN10: 1799801829|ISBN13 Softcover: 9781799801832|EISBN13: 9781799801849
DOI: 10.4018/978-1-7998-0182-5.ch001
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MLA

Ong, Pauline, et al. "Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia." Challenges and Applications for Implementing Machine Learning in Computer Vision, edited by Ramgopal Kashyap and A.V. Senthil Kumar, IGI Global, 2020, pp. 1-42. https://doi.org/10.4018/978-1-7998-0182-5.ch001

APA

Ong, P., Chong, T. W., & Lee, W. K. (2020). Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia. In R. Kashyap & A. Kumar (Eds.), Challenges and Applications for Implementing Machine Learning in Computer Vision (pp. 1-42). IGI Global. https://doi.org/10.4018/978-1-7998-0182-5.ch001

Chicago

Ong, Pauline, Tze Wei Chong, and Woon Kiow Lee. "Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia." In Challenges and Applications for Implementing Machine Learning in Computer Vision, edited by Ramgopal Kashyap and A.V. Senthil Kumar, 1-42. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0182-5.ch001

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Abstract

The traditional approach of student attendance monitoring system in Universiti Tun Hussein Onn Malaysia is slow and disruptive. As a solution, biometric verification based on face recognition for student attendance monitoring was presented. The face recognition system consisted of five main stages. Firstly, face images under various conditions were acquired. Next, face detection was performed using the Viola Jones algorithm to detect the face in the original image. The original image was minimized and transformed into grayscale for faster computation. Histogram techniques of oriented gradients was applied to extract the features from the grayscale images, followed by the principal component analysis (PCA) in dimension reduction stage. Face recognition, the last stage of the entire system, using support vector machine (SVM) as classifier. The development of a graphical user interface for student attendance monitoring was also involved. The highest face recognition accuracy of 62% was achieved. The obtained results are less promising which warrants further analysis and improvement.

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