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Robust Real-Time Face Detection
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“In 2001, P. Viola and M. Jones achieved real-time detection of human faces for the first time without any constraints (e.g., skin color segmentation) [10, 11].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [11]
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. (1997) · European Conference on Computational Learning Theory · doi:10.1007/3-540-59119-2_166 · Cited by 23,988
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
The design and use of steerable filters
Freeman, W. T., & Adelson, E. H. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.93808 · Cited by 2,937
Boosting the margin: a new explanation for the effectiveness of voting methods
Bartlett, P. L., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,574
Training support vector machines: an application to face detection
Osuna, E., Freund, R. M., & Girosi, F. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1997.609310 · Cited by 2,510
Irrelevant Features and the Subset Selection Problem
John, G. H., Kohavi, R., & Pfleger, K. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50023-4 · Cited by 2,403
Example-based learning for view-based human face detection
Sung, K.-K., & Poggio, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655648 · Cited by 1,784
Research works printed but not linked: 2
- Osuna, E., Freund, R., and Girosi, F. 1997b. Training support vector machines: an application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
- Roth, D., Yang, M., and Ahuja, N. 2000. A snowbased face detector. In Neural Information Processing 12.
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Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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de Haan, G., & Jeanne, V. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2266196 · Cited by 1,378
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Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269
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Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205
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Hao, Z., Liu, Y., Qin, H., Yan, J., Li, X., & Hu, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.207 · Cited by 134
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Wu, T., Bartlett, M. S., & Movellan, J. R. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543267 · Cited by 126
Li, J., Wang, Y., Lei, B., Cheng, J.-Z., Qin, J., Wang, T., Li, S., & Ni, D. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2703890 · Cited by 120
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Metadata
- Authors
- Viola, P., & Jones, M. J.
- Affiliations
- Microsoft (United States) (US)
- Mitsubishi Electric Research Laboratories (United States) (US)
- ORCID
- Jones, Michael J.
- Venue
- International Journal of Computer Vision
- Published
- 2004-05
- Volume
- 57
- Issue
- 2
- Pages
- 137-154
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W3097096317
- Cited by
- 14,097
- References
- 22
- Citations by year
- 2026: 104 · 2025: 212 · 2024: 280 · 2023: 390 · 2022: 517 · 2021: 640 · 2020: 675 · 2019: 837 · 2018: 848 · 2017: 954 · 2016: 945 · 2015: 1115 · 2014: 1048 · 2013: 993 · 2012: 933
- FWCI
- 290.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face detection, real-time detection, integral image, AdaBoost, feature selection, background suppression, computational efficiency
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