The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Robust Real-Time Face Detection
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
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
Flagged first, then by citations
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.
Cited by
Sorted by citations
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
“Detectors for single classes like faces or people can be highly optimized since they have to deal with much less variation [36].”
BackgroundThe pascal visual object classes (VOC) challenge
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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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