FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN
Face detection is a crucial task in various applications, including face recognition, facial expression analysis, face tracking, and head-pose estimation, spanning fields such as transport, health, and education. Conventional face detectors, from Viola-Jones to CNN-based methods, face challenges in...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n1/46.pdf |
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| author | Nipun Singhal Nidhi Gupta Ajay Kumar Singh |
| author_facet | Nipun Singhal Nidhi Gupta Ajay Kumar Singh |
| author_sort | Nipun Singhal |
| collection | DOAJ |
| description | Face detection is a crucial task in various applications, including face recognition, facial expression analysis, face tracking, and head-pose estimation, spanning fields such as transport, health, and education. Conventional face detectors, from Viola-Jones to CNN-based methods, face challenges in handling diverse facial characteristics in "in the wild" scenarios due to the surge in image and video data. The advent of deep learning brings advancements in face detection, albeit with increased computational demands. This paper provides an overview of deep learning-based methods, offering a comprehensive analysis of their accuracy and efficiency. It delves into a comparative discussion of challenging datasets and associated evaluation metrics. A detailed examination of the efficiency of successful deep learning-based face detectors, including CNN, MTCNN, and Haar cascade, is conducted. The insights gained from this analysis guide the selection of appropriate face detectors for diverse applications and lay the foundation for developing more efficient and accurate detectors in the future. |
| format | Article |
| id | doaj-art-24a2e65a9cd6477eaf16f39f0f9f1b0c |
| institution | OA Journals |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-24a2e65a9cd6477eaf16f39f0f9f1b0c2025-08-20T01:57:35ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017142743610.24874/PES07.01C.009FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNNNipun Singhal 0https://orcid.org/0009-0002-9873-4610Nidhi Gupta 1https://orcid.org/0000-0003-1764-2555Ajay Kumar Singh 2https://orcid.org/0009-0001-4617-6293Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India Face detection is a crucial task in various applications, including face recognition, facial expression analysis, face tracking, and head-pose estimation, spanning fields such as transport, health, and education. Conventional face detectors, from Viola-Jones to CNN-based methods, face challenges in handling diverse facial characteristics in "in the wild" scenarios due to the surge in image and video data. The advent of deep learning brings advancements in face detection, albeit with increased computational demands. This paper provides an overview of deep learning-based methods, offering a comprehensive analysis of their accuracy and efficiency. It delves into a comparative discussion of challenging datasets and associated evaluation metrics. A detailed examination of the efficiency of successful deep learning-based face detectors, including CNN, MTCNN, and Haar cascade, is conducted. The insights gained from this analysis guide the selection of appropriate face detectors for diverse applications and lay the foundation for developing more efficient and accurate detectors in the future.https://pesjournal.net/journal/v7-n1/46.pdffacial detectionconvolutional neural networkshaar cascademtcnncomputer visionimage processingbiometricsface recognition |
| spellingShingle | Nipun Singhal Nidhi Gupta Ajay Kumar Singh FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN Proceedings on Engineering Sciences facial detection convolutional neural networks haar cascade mtcnn computer vision image processing biometrics face recognition |
| title | FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN |
| title_full | FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN |
| title_fullStr | FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN |
| title_full_unstemmed | FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN |
| title_short | FACIAL DETECTION IN COMPUTER VISION: BRIDGING GAP BETWEEN CNN, HAAR CASCADE AND MTCNN |
| title_sort | facial detection in computer vision bridging gap between cnn haar cascade and mtcnn |
| topic | facial detection convolutional neural networks haar cascade mtcnn computer vision image processing biometrics face recognition |
| url | https://pesjournal.net/journal/v7-n1/46.pdf |
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