Deep-learning-CNN for detecting covered faces with niqab
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 fac...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Tehran
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/103310/1/MohdShahrizalSunar2022_DeepLearningCNNforDetectingCovered.pdf http://eprints.utm.my/103310/ http://dx.doi.org/10.22059/JITM.2022.84888 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | 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. |
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