Neural Network-Based Face Detection

Henry Rowley, Shumeet Baluja, and Takeo Kanade
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, January, 1998, pp. 23-38.


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Abstract
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.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Face Group
Associated Project(s): Neural Network-Based Face Detection, Face Detection Databases, Face Detection, Face Databases

Text Reference
Henry Rowley, Shumeet Baluja, and Takeo Kanade, "Neural Network-Based Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, January, 1998, pp. 23-38.

BibTeX Reference
@article{Rowley_1998_926,
   author = "Henry Rowley and Shumeet Baluja and Takeo Kanade",
   title = "Neural Network-Based Face Detection",
   journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
   pages = "23-38",
   month = "January",
   year = "1998",
   volume = "20",
   number = "1",
}