The Robotics Institute

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

Sponsor: Siemens Corporate Research, Inc., Department of the Army, Army Research Office DAAH04-94-G-0006, and Office of Naval Research N00014-95-1-0591

Associated Project(s): Neural Network-Based Face Detection

Text Reference

Henry Rowley, Shumeet Baluja, and Takeo Kanade, "Human Face Detection in Visual Scenes," tech. report CMU-CS-95-158R, Computer Science Department, Carnegie Mellon University, November, 1995

BibTeX Reference

@techreport{Rowley_1995_1461,
   author = "Henry Rowley and Shumeet Baluja and Takeo Kanade",
   title = "Human Face Detection in Visual Scenes",
   booktitle = "",
   institution = "Computer Science Department",
   month = "November",
   year = "1995",
   number= "CMU-CS-95-158R",
   address= "Pittsburgh, PA",
}

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