Share Email Print

Proceedings Paper

Constructive Hermite polynomial feedforward neural networks with application to facial expression recognition
Author(s): Liying Ma; Khashayar Khorasani
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Computer-based recognition of human facial expressions has been an active area of research since the 1970s. The ultimate goal is to realize intelligent man-machine interface. Recently, constructive One-Hidden-Layer Feedforward Neural Networks (OHL-FNNs) have been found promising for facial expression recognition. The hidden units in a FNN usually have the same activation functions typically selected as sigmoidal functions. However, it has not been proven that the use of the same activation functions for all the hidden units is the best or optimal choice in terms of network performance. In this paper, a new constructive polynomial OHL-FNN is proposed for pattern recognition. The well-known Hermite polynomials will be used as activation functions for the hidden units. Each time a new hidden unit is to be added to the network, a Hermite polynomial whose order is increased by one will be used as the activation function of the hidden unit. The proposed technique is applied to the facial expression recognition problem where the 2D DCT is performed over the entire face image before the resulting lower 2D DCT coefficients are fed to the constructive network training. The advantages and limitations of the constructive polynomial OHL-FNN for pattern recognition are also discussed.

Paper Details

Date Published: 12 November 2001
PDF: 12 pages
Proc. SPIE 4520, Video Technologies for Multimedia Applications, (12 November 2001); doi: 10.1117/12.448231
Show Author Affiliations
Liying Ma, Concordia Univ. (Canada)
Khashayar Khorasani, Concordia Univ. (Canada)

Published in SPIE Proceedings Vol. 4520:
Video Technologies for Multimedia Applications
Mahmood R. Azimi-Sadjadi; Sassan Sheedvash, Editor(s)

© SPIE. Terms of Use
Back to Top