Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions
As computer vision and machine learning advance, face detection has become a major focus. Face recognition has several methods and models. Every implementation starts with face detection. Haar Cascades and Multi-task Cascaded Convolutional Networks (MTCNN) are compared for facial pose variation robu...
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| Format: | Article |
| Language: | English |
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Croatian Communications and Information Society (CCIS)
2025-03-01
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| Series: | Journal of Communications Software and Systems |
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| Online Access: | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0084/ |
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| author | Omer Abdulhaleem Naser Sharifah Mumtazah Khairulmizam Samsudin Marsyita Hanafi Siti Mariam Binti Nor Zarina Zamri |
| author_facet | Omer Abdulhaleem Naser Sharifah Mumtazah Khairulmizam Samsudin Marsyita Hanafi Siti Mariam Binti Nor Zarina Zamri |
| author_sort | Omer Abdulhaleem Naser |
| collection | DOAJ |
| description | As computer vision and machine learning advance, face detection has become a major focus. Face recognition has several methods and models. Every implementation starts with face detection. Haar Cascades and Multi-task Cascaded Convolutional Networks (MTCNN) are compared for facial pose variation robustness. This research will examine how well these two models detect faces in yaw postures from -90 to +90 degrees. Many studies have contrasted these two models, but the yaw poses of faces were not addressed due to the scarcity of datasets with systematic degrees of face orientation. Thus, the UPM face dataset, created at the UPM embedded systems lab using developed equipment to produce high-resolution photographs and a systematic range of face orientations from -90 to 90 degrees, was used to evaluate the range of degrees these two models can reach. UPM includes 100 students with different yaw angles and occlusions (masks, glasses, or both). The results reveal that MTCNN is the best for detecting faces with yaw poses only, masks, glasses, and both at all degrees (-90 to +90) with 100%, 99.9%, 96.4%, and 80% accuracy. Instead, Haar cascades were 92.5%, 67.3%, 80.4%, and 76.3% accurate. |
| format | Article |
| id | doaj-art-20ba4e0985554dbe926e97e2b1277e4d |
| institution | OA Journals |
| issn | 1845-6421 1846-6079 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Croatian Communications and Information Society (CCIS) |
| record_format | Article |
| series | Journal of Communications Software and Systems |
| spelling | doaj-art-20ba4e0985554dbe926e97e2b1277e4d2025-08-20T01:50:06ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792025-03-0121110911910.24138/jcomss-2024-0084Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial OcclusionsOmer Abdulhaleem NaserSharifah MumtazahKhairulmizam SamsudinMarsyita HanafiSiti Mariam BintiNor Zarina ZamriAs computer vision and machine learning advance, face detection has become a major focus. Face recognition has several methods and models. Every implementation starts with face detection. Haar Cascades and Multi-task Cascaded Convolutional Networks (MTCNN) are compared for facial pose variation robustness. This research will examine how well these two models detect faces in yaw postures from -90 to +90 degrees. Many studies have contrasted these two models, but the yaw poses of faces were not addressed due to the scarcity of datasets with systematic degrees of face orientation. Thus, the UPM face dataset, created at the UPM embedded systems lab using developed equipment to produce high-resolution photographs and a systematic range of face orientations from -90 to 90 degrees, was used to evaluate the range of degrees these two models can reach. UPM includes 100 students with different yaw angles and occlusions (masks, glasses, or both). The results reveal that MTCNN is the best for detecting faces with yaw poses only, masks, glasses, and both at all degrees (-90 to +90) with 100%, 99.9%, 96.4%, and 80% accuracy. Instead, Haar cascades were 92.5%, 67.3%, 80.4%, and 76.3% accurate.https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0084/face detectionfacial occlusionshaar cascadesmtcnnoccluded facesupm datasetyaw poses |
| spellingShingle | Omer Abdulhaleem Naser Sharifah Mumtazah Khairulmizam Samsudin Marsyita Hanafi Siti Mariam Binti Nor Zarina Zamri Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions Journal of Communications Software and Systems face detection facial occlusions haar cascades mtcnn occluded faces upm dataset yaw poses |
| title | Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions |
| title_full | Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions |
| title_fullStr | Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions |
| title_full_unstemmed | Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions |
| title_short | Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions |
| title_sort | comparative analysis of mtcnn and haar cascades for face detection in images with variation in yaw poses and facial occlusions |
| topic | face detection facial occlusions haar cascades mtcnn occluded faces upm dataset yaw poses |
| url | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0084/ |
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