Deep-Learning-CNN for Detecting Covered Faces with Niqab

Document Type : Research Paper

Authors

1 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.

2 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.

3 Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, 81310 Skudai, Johor, Malaysia.

Abstract

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

Keywords

References

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