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Proceedings Paper

Real-time camera-based face detection using a modified LAMSTAR neural network system
Author(s): Javier I. Girado; Daniel J. Sandin; Thomas A. DeFanti; Laura K. Wolf
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Paper Abstract

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

Paper Details

Date Published: 25 March 2003
PDF: 11 pages
Proc. SPIE 5015, Applications of Artificial Neural Networks in Image Processing VIII, (25 March 2003); doi: 10.1117/12.477405
Show Author Affiliations
Javier I. Girado, Univ. of Illinois/Chicago (United States)
Daniel J. Sandin, Univ. of Illinois/Chicago (United States)
Thomas A. DeFanti, Univ. of Illinois/Chicago (United States)
Laura K. Wolf, Univ. of Illinois/Chicago (United States)

Published in SPIE Proceedings Vol. 5015:
Applications of Artificial Neural Networks in Image Processing VIII
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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