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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, 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 another state-of-the-art face detection system 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, "Human Face Detection in Visual Scenes," Advances in Neural Information Processing Systems 8, 1996, pp. 875 - 881.

BibTeX Reference

@inproceedings{Rowley_1996_2677,
   author = "Henry Rowley and Shumeet Baluja and Takeo Kanade",
   title = "Human Face Detection in Visual Scenes",
   booktitle = "Advances in Neural Information Processing Systems 8",
   pages = "875 - 881",
   year = "1996",
}

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