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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The use of CNNs for detection and localization can be traced back to the 1990s, with a modest number of hidden layers, successful in restricted domains such as face detection.
“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
Before 1990 the leading paradigm of object recognition was based on geometric representations, with focus later moving away from geometry and prior models towards statistical classifiers based on appearance features.
“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
Recently, the problem of automatic face detection in an arbitrary scene has drawn great attention.
“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]
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- 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
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.
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