A. Alashbi - Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, ۸۱۳۱۰ Skudai, Johor, Malaysia. ۲School of Computing, Faculty of Engineering, University Technology Malaysia,
Sunar - Professor, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, ۸۱۳۱۰ Skudai, Johor, Malaysia. ۲School of Computing, Faculty of Engineering, University Technology Malaysia,
Alqahtani - Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, ۸۱۳۱۰ Skudai, Johor, Malaysia.

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

Face-detection, Object-detection, Computer Vison, Deep learning, Artificial Intelligence, Convolutional Neural Network