An accurate active shape model for facial feature extraction

Kwok Wai Wan, Kin Man Lam, Kit Chong Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

60 Citations (Scopus)

Abstract

The active shape model (ASM) has been used successfully to extract the facial features of a face image under frontal view. However, its performance degrades when the face concerned is under perspective variations. In this paper, a modified shape model is proposed which can adapt to face images under different orientations. To make the model represent a face more flexibly, the representations of the important facial features, i.e. the eyes, nose and mouth, and the face contour are separated. An energy function is defined that links up these two representations of a human face. In order to represent a face image under different poses, three models are employed to represent the important facial features: the left-viewed, right-viewed, and frontal-viewed models. The genetic algorithm (GA) is applied to search for the best representation of face images. Experimental results demonstrate that our proposed method can achieve a better performance in representing face images under different perspective variations and facial expressions than the conventional ASM can.
Original languageEnglish
Pages (from-to)2409-2423
Number of pages15
JournalPattern Recognition Letters
Volume26
Issue number15
DOIs
Publication statusPublished - 1 Nov 2005

Keywords

  • Active shape model
  • Facial feature extraction
  • Genetic algorithm

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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