Cited by 3,554Face recognition and analysisFace and Expression RecognitionFace Recognition and Perception

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Neural network-based face detection

Rowley, H.A., Baluja, S., Kanade, T.·1998·IEEE Transactions on Pattern Analysis and Machine Intelligence

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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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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
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 1998
Volume: 20
Issue: 1
Pages: 23-38
ISSN: 0162-8828 (ISSN-L)
DOI: 10.1109/34.655647
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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Neural network-based face detection

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