Mohammad A. Alia

Work place: Faculty of Sciences & IT, Al-Zaytoonah University of Jordan, Dept. of Computer Information Systems, P.O. Box130, Amman (11733), Jordan



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Network Security, Data Structures and Algorithms


 Dr. Mohammad Alia is an associate professor at the Computer Information Systems department, Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan. He received the B.Sc. degree in Computer Science from Al-Zaytoonah University, Jordan, in 2000. He obtained his Ph.D. degree in Computer Science from University Science of Malaysia, in 2008. During 2000 until 2004, he worked at Al-Zaytoonah University of Jordan as an instructor of Computer Sciences and Information Technology. Then, he worked as a lecturer at Al-Quds University in Saudi Arabia from 2004 to 2005. Currently he is working as the Chair of Computer Information Systems dept. at Al Zaytoonah University of Jordan. His research interests are in the field of Cryptography and Network Security.

Author Articles
Real-Time Group Face-Detection for an Intelligent Class-Attendance System

By Abdelfatah Aref Tamimi Omaima N. A. AL-Allaf Mohammad A. Alia

DOI:, Pub. Date: 8 May 2015

The traditional manual attendance system wastes time over students’ responses, but it has worked well for small numbers of students. This research presents a real-time group face-detection system. This system will be used later for student class attendance through automatic student identification. The system architecture and its algorithm will be described in details. The algorithm for the system was based on analyzing facial properties and features in order to perform face detection for checking students’ attendance in real time. The classroom’s camera captures the students’ photo. Then, face detection will be implemented automatically to generate a list of detected student faces. Many experiments were adopted based on real time video captured using digital cameras. The experimental results showed that our approach of face detection offers real-time processing speed with good acceptable detection ratio equal to 94.73%.

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