Extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions

Rosdiyana Samad, Hideyuki Sawada*

*この研究の対応する著者

研究成果: Article査読

19 被引用数 (Scopus)

抄録

Facial expression recognition has recently become an important research area, and many efforts have been made in facial feature extraction and its classification to improve face recognition systems. Most researchers adopt a posed facial expression database in their experiments, but in a real-life situation the facial expressions may not be very obvious. This article describes the extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions. The objective of our research was to investigate the performance of a facial expression recognition system with a minimum number of features of the Gabor wavelet. In this research, principal component analysis (PCA) is employed to compress the Gabor features. We also discuss the selection of the minimum number of Gabor features that will perform the best in a recognition task employing a multiclass support vector machine (SVM) classifier. The performance of facial expression recognition using our approach is compared with those obtained previously by other researchers using other approaches. Experimental results showed that our proposed technique is successful in recognizing natural facial expressions by using a small number of Gabor features with an 81.7% recognition rate. In addition, we identify the relationship between the human vision and computer vision in recognizing natural facial expressions.

本文言語English
ページ(範囲)21-31
ページ数11
ジャーナルArtificial Life and Robotics
16
1
DOI
出版ステータスPublished - 2011 6 1
外部発表はい

ASJC Scopus subject areas

  • 生化学、遺伝学、分子生物学(全般)
  • 人工知能

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