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Carnegie Mellon Robotics Institute

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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.

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," Proceedings of the 1996 DARPA Image Understanding Workshop, February, 1996.

BibTeX Reference

@inproceedings{Rowley_1996_2679,
   author = "Henry Rowley and Shumeet Baluja and Takeo Kanade",
   title = "Neural Network-Based Face Detection",
   booktitle = "Proceedings of the 1996 DARPA Image Understanding Workshop",
   month = "February",
   year = "1996",
}

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