Neural Network-Based Face Detection

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.