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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| Format: | Article |
| Language: | English |
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University of Tehran
2022-02-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_84888_a3b5d00476d6628dea08b1dcf27a9c27.pdf |
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| author | Abdulaziz A. Alashbi Mohd Shahrizal Sunar Zieb Alqahtani |
| author_facet | Abdulaziz A. Alashbi Mohd Shahrizal Sunar Zieb Alqahtani |
| author_sort | Abdulaziz A. Alashbi |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-8d75395d912740d18cd7aaa3b1ed6eb4 |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-8d75395d912740d18cd7aaa3b1ed6eb42025-08-20T01:58:30ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592022-02-01145th International Conference of Reliable Information and Communication Technology (IRICT 2020)11412310.22059/jitm.2022.8488884888Deep-Learning-CNN for Detecting Covered Faces with NiqabAbdulaziz A. Alashbi0Mohd Shahrizal Sunar1Zieb Alqahtani2Ph.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.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.Ph.D. Candidate, Media and Game Innovation Centre of Excellence, Institute of Human Centered Engineering, University Technology Malaysia, 81310 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 algorithmshttps://jitm.ut.ac.ir/article_84888_a3b5d00476d6628dea08b1dcf27a9c27.pdfface-detectionobject-detectioncomputer visondeep learningartificial intelligenceconvolutional neural network |
| spellingShingle | Abdulaziz A. Alashbi Mohd Shahrizal Sunar Zieb Alqahtani Deep-Learning-CNN for Detecting Covered Faces with Niqab Journal of Information Technology Management face-detection object-detection computer vison deep learning artificial intelligence convolutional neural network |
| title | Deep-Learning-CNN for Detecting Covered Faces with Niqab |
| title_full | Deep-Learning-CNN for Detecting Covered Faces with Niqab |
| title_fullStr | Deep-Learning-CNN for Detecting Covered Faces with Niqab |
| title_full_unstemmed | Deep-Learning-CNN for Detecting Covered Faces with Niqab |
| title_short | Deep-Learning-CNN for Detecting Covered Faces with Niqab |
| title_sort | deep learning cnn for detecting covered faces with niqab |
| topic | face-detection object-detection computer vison deep learning artificial intelligence convolutional neural network |
| url | https://jitm.ut.ac.ir/article_84888_a3b5d00476d6628dea08b1dcf27a9c27.pdf |
| work_keys_str_mv | AT abdulazizaalashbi deeplearningcnnfordetectingcoveredfaceswithniqab AT mohdshahrizalsunar deeplearningcnnfordetectingcoveredfaceswithniqab AT ziebalqahtani deeplearningcnnfordetectingcoveredfaceswithniqab |