The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Neural network-based face detection
Abstract
We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
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In the citing paper
“The use of CNNs for detection and localization (Rowley et al. 1998) can be traced back to the 1990s, with a modest number of hidden layers used for object detection (Vaillant et al. 1994;Rowley et al. 1998;Sermanet et al. 2013), successful in restricted domains such as face detec-tion.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Rowley et al. 1998
In the citing paper
“Before 1990 the leading paradigm of object recognition was based on geometric representations (Mundy 2006;Ponce et al. 2007), with the focus later moving away from geometry and prior models towards the use of statistical classifiers [such as Neural Networks (Rowley et al. 1998), SVM (Osuna et al. 1997) and Adaboost (Viola and Jones 2001;Xiao et al. 2003)] based on appearance features (Murase and N”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Rowley et al. 1998
In the citing paper
“Recently, the problem of automatic face detection in an arbitrary scene has drawn great attention (e.g., see [74], [83], [85]).”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [85]
Citations
Cites
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Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643
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
The FERET database and evaluation procedure for face-recognition algorithms
Phillips, P. J., Wechsler, H., Huang, J., & Rauss, P. J. (1998) · Image and Vision Computing · doi:10.1016/s0262-8856(97)00070-x · Cited by 2,435
View-based and modular eigenspaces for face recognition
Pentland, Moghaddam, & Starner (1994) · doi:10.1109/cvpr.1994.323814 · Cited by 1,806
Baluja, S. (1994) · Cited by 1,309
Self-citationThree-dimensional object recognition
Besl, P. J., & Jain, R. (1985) · ACM Computing Surveys · doi:10.1145/4078.4081 · Cited by 970
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Research works printed but not linked: 1
- sinha, 1994, object recognition via image invariants: a case study, Investigative Ophthalmology and Visual Science, 35
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Human Face Detection and Recognition using Web-Cam
S. (2012) · Journal of Computer Science · doi:10.3844/jcssp.2012.1585.1593 · Cited by 21
Understanding trust in privacy-aware video surveillance systems
Rashwan, H. A., Solanas, A., Puig, D., & Martínez-Ballesté, A. (2015) · International Journal of Information Security · doi:10.1007/s10207-015-0286-9 · Cited by 20
DistillSpec: Improving Speculative Decoding via Knowledge Distillation
Zhou, Y., Lyu, K., Rawat, A. S., Menon, A. K., Rostamizadeh, A., Kumar, S., Kagy, J.-F., & Agarwal, R. (2023) · arXiv · doi:10.48550/arxiv.2310.08461 · Cited by 9
DenseBox: Unifying Landmark Localization with End to End Object Detection
Huang, L., Yang, Y., Deng, Y., & Yu, Y. (2015) · arXiv · doi:10.48550/arxiv.1509.04874 · Cited by 407
Metadata
- Authors
- Rowley, H. A., Baluja, S., & Kanade, T.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Baluja, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998
- Volume
- 20
- Issue
- 1
- Pages
- 23-38
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2217896605
- Cited by
- 3,554
- References
- 47
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 29 · 2023: 47 · 2022: 58 · 2021: 89 · 2020: 80 · 2019: 92 · 2018: 113 · 2017: 135 · 2016: 112 · 2015: 135 · 2014: 160 · 2013: 141 · 2012: 156
- FWCI
- 92.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Funders
- National Science Foundation
- Office of Naval Research
- Army Research Office
- 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, neural network ensembles, negative example selection, false detection, false positive rate
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