Advancing Attendance: A Facial Recognition System Empowered by Deep Learning Techniques


  • Huda Al-Nayyef Department of Computer Science, College of Science, Mustansiriyah University, 10052 Baghdad, Iraq



Attendance system,, Face recognition,, Deep learning,, convolutional neural networks (CNN),, Haar-Cascade classifier,, Local Binary Pattern Histogram (LBPH),, Histograms of Oriented Gradient (HOG).


Many studies during the last decade tried to provide an automatic technique for facial recognition and identification challenge especially in security systems. In this study, we suggested two methods for student attendance problem based on image processing and machine learning algorithms. The first method uses Haar cascade classifier with the Local Binary Patterns Histograms (LBPH) model and the second method composed from Histograms of Oriented Gradient (HoG) followed by the Convolutional Neural Network (CNN) model. Both methods take a collection of random student images taken from low quality sources as input. A set of image processing filters are first applied on images to enhance the method of extracting the face boundary. Then, each model will be trained using random images from student dataset. The trained model is tested using testing set. The results showed that the method that employ CNN model with HoG provides high accuracy value of 98.44%. While, the accuracy of LBPH model with Haar Cascade classifier is 95.63%.


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How to Cite

Al-Nayyef, H. (2024). Advancing Attendance: A Facial Recognition System Empowered by Deep Learning Techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(1), Comp. 61–71 .



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