Real-time camera-based face detection using a modified LAMSTAR neural network system

Javier I. Girado; Daniel J. Sandin; Thomas A. DeFanti; Laura K. Wolf

doi:10.1117/12.477405

25 March 2003 Real-time camera-based face detection using a modified LAMSTAR neural network system

Javier I. Girado, Daniel J. Sandin, Thomas A. DeFanti, Laura K. Wolf

Proceedings Volume 5015, Applications of Artificial Neural Networks in Image Processing VIII; (2003) https://doi.org/10.1117/12.477405
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

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.

Citation Download Citation

Javier I. Girado, Daniel J. Sandin, Thomas A. DeFanti, and Laura K. Wolf "Real-time camera-based face detection using a modified LAMSTAR neural network system", Proc. SPIE 5015, Applications of Artificial Neural Networks in Image Processing VIII, (25 March 2003); https://doi.org/10.1117/12.477405

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