AI summary

This paper introduces a neural network-based face detection system using a retinally connected neural network, a bootstrap algorithm for training, and arbitration between multiple networks. It achieves better performance in terms of detection and false-positive rates compared to other state-of-the-art face detection systems.

Abstract

We present a neural network-based 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 use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with other state-of-the-art face detection systems are presented; our system has better performance in terms of detection and false-positive rates.