Neural network-based face detection

作者：Henry A. Rowley, Shumeet Baluja, Takeo Kanade · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：1998 · DOI：10.1109/34.655647 · 被引用次数：3533 · 研究领域：Face recognition and analysis、Face and Expression Recognition、Biometric Identification and Security

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.