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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 systemarbitrates 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.