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  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20032003:821085

  • Received: 22 February 2001
  • Published:


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

Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, QLD, 4001, Australia


© Copyright © 2003 Hindawi Publishing Corporation 2003