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

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.

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Correspondence to Simon Lucey.

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Lucey, S., Sridharan, S. & Chandran, V. Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis. EURASIP J. Adv. Signal Process. 2003, 821085 (2003). https://doi.org/10.1155/S1110865703209045

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Keywords and phrases

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