A Comprehensive Face Recognition Solution for Attendance and Social Security System Using CNN


  • Md Serajun Nabi School of Computing and Informatics Albukhary International University Author
  • Golam Md Mohiuddin School of Computing and Informatics Albukhary International University Author
  • Zuberi Mfaki School of Computing and Informatics Albukhary International University Author
  • Akibu Mahmoud Abdullahi School of Computing and Informatics Albukhary International University Author




Face Recognition, student, social contribution, Convolutional Neural Network, OpenCV


Face recognition is one of the most important applications of image processing in the technical world. Face recognition can be used for a variety of purposes, including access control systems, improve security, law enforcement, forensic investigations, health services, identity identification, and so on. The primary purpose of this project is to develop an attendance system based on face recognition for educational institutions to improve and modernize the existing attendance system. Regular attendance logs are vitally crucial to educational institutions, even though they are complicated and timeconsuming to operate. There are several automated methods for identifying students, including speech recognition, Radio Frequency Identification (RFID), eye tracking, and biometrics. In today’s world, the students are losing their time to get more study and getting trouble in-terms of social security. A face is one of the most frequently utilized biometrics for confirming a student's academic attendance and social security. In this project, face databases were created from the Computer science student of Albukhary International University, as well as the data were preprocessed based on the student full address, such as, name, ID, and program in order to input data into the recognizer algorithm. To implement this system, the machine learning algorithm was trained and test, which is a Convolutional Neural Network and OpenCV library with the accuracy of 94%. The system is capable to successfully recognize multiple students' faces for their daily attendance. The system gives overall accuracy of 94 percent in normal condition with facial expression and wearing glasses but there is some limitation as well, which are twin faces and with beard.


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How to Cite

A Comprehensive Face Recognition Solution for Attendance and Social Security System Using CNN. (2024). Malaysian Journal of Information and Communication Technology (MyJICT), 8(2), 8-22. https://doi.org/10.53840/myjict8-2-29