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

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

EURASIP Journal on Applied Signal Processing 2003:3, 264–275 c ○ 2003 Hindawi Publishing Corporation Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis

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 postprocessing task such as speech or speaker recognition. With these issues in mind, this paper presents a framework for a facialfeature 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