TY - JOUR ID - 84888 TI - Deep-Learning-CNN for Detecting Covered Faces with Niqab JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - A. Alashbi, Abdulaziz AU - Sunar, Mohd Shahrizal AU - Alqahtani, Zieb AD - Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, 81310 Skudai, Johor, Malaysia. 2School of Computing, Faculty of Engineering, University Technology Malaysia, 81310, Skudai, Johor, Malaysia. AD - Professor, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, 81310 Skudai, Johor, Malaysia. 2School of Computing, Faculty of Engineering, University Technology Malaysia, 81310, Skudai, Johor, Malaysia. AD - Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, 81310 Skudai, Johor, Malaysia. Y1 - 2022 PY - 2022 VL - 14 IS - Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020) SP - 114 EP - 123 KW - Face-detection KW - Object-detection KW - Computer Vison KW - Deep learning KW - Artificial Intelligence KW - Convolutional Neural Network DO - 10.22059/jitm.2022.84888 N2 - Detecting occluded faces is a non-trivial problem for face detection in computer vision. This challenge becomes more difficult when the occlusion covers majority of the face. Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. In this paper, a deep-learning-face-detection model Niqab-Face-Detector is proposed along with context-based labeling technique for detecting unconstrained veiled faces such as faces covered with niqab. An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms UR - https://jitm.ut.ac.ir/article_84888.html L1 - https://jitm.ut.ac.ir/article_84888_a3b5d00476d6628dea08b1dcf27a9c27.pdf ER -