Open Access

Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis

EURASIP Journal on Advances in Signal Processing20032003:821085

DOI: 10.1155/S1110865703209045

Received: 22 February 2001

Published: 13 March 2003

Abstract

An integral part of any audio-visual speech processing (AVSP) system is the front-end visual system that detects facial-features (e.g., eyes and mouth) pertinent to the task of visual speech processing. The ability of this front-end system to not only locate, but also give a confidence measure that the facial-feature is present in the image, directly affects the ability of any subsequent post-processing task such as speech or speaker recognition. With these issues in mind, this paper presents a framework for a facial-feature detection system suitable for use in an AVSP system, but whose basic framework is useful for any application requiring frontal facial-feature detection. A novel approach for facial-feature detection is presented, based on an appearance paradigm. This approach, based on intraclass unsupervised clustering and discriminant analysis, displays improved detection performance over conventional techniques.

Keywords and phrases

audio-visual speech processing facial-feature detection unsupervised clustering discriminant analysis

Authors’ Affiliations

(1)
Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering, Queensland University of Technology

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003