Neural network-based face detection
Rowley, H.A.; Baluja, S.; Kanade, T.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 20, Issue 1, Jan 1998 Page(s):23 - 38
Digital Object Identifier 10.1109/34.655647
Summary: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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