NEURAL-NETWORK-BASED FACE DETECTION

We present a neural network-based upright frontal face detection syste m. A retinally connected neural network examines small windows of an i mage and decides whether each window contains a face. The system arbit rates between multiple networks to improve performance over a single n etwork. We present a straightforward procedure for aligning positive f ace examples for training. To collect negative examples, we use a boot strap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually se lecting nonface training examples, which must be chosen to span the en tire space of nonface images. Simple heuristics, such as using the fac t that faces rarely overlap in images, can further improve the accurac y. Comparisons with several other state-of-the-art face detection syst ems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.