Human Face Detection in Visual Scenes

Henry A. Rowley, har@cs.cmu.edu
Shumeet Baluja, baluja@cs.cmu.edu
Takeo Kanade, tk@cs.cmu.edu

November 1995

Carnegie Mellon Computer Science Technical Report CMU-CS-95-158R

Keywords: Face detection, Pattern recognition, Computer vision, Artificial neural networks, Machine learning

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.

This work was partially supported by a grant from Siemens Corporate Research, Inc., by the Department of the Army, Army Research Office under grant number DAAH04-94-G-0006, and by the Office of Naval Research under grant number N00014-95-1-0591. This work was started while Shumeet Baluja was supported by a National Science Foundation Graduate Fellowship. He is currently supported by a graduate student fellowship from the National Aeronautics and Space Administration, administered by the Lyndon B. Johnson Space Center. The views and conclusions contained in this document are those of the authors, and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of the sponsoring agencies.

Henry A Rowley
Sun Nov 26 00:21:42 EST 1995