Edge-based facial feature extraction using Gabor wavelet and convolution filters

Rosdiyana Samad*, Hideyuki Sawada

*この研究の対応する著者

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

Feature extraction is a crucial step for many systems of face detection and facial expression recognition. In this paper, we present edge-based feature extraction for recognizing six different expressions, which are angry, fear, happy, neutral, sadness and surprise. Edge detection is performed by using Gabor wavelet and convolution filters. In this paper we propose two convolution kernels that are specific for the edge detection of facial components in two orientations. In this study, Principal Component Analysis (PCA) is used to reduce the features dimension. To validate the performance of our proposed feature extraction, the generated features are classified using Support Vector Machine. The experimental results demonstrated that the proposed feature extraction method could generate significant facial features and these features are able to be classified into each expression.

本文言語English
ホスト出版物のタイトルProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
ページ430-433
ページ数4
出版ステータスPublished - 2011 12月 1
外部発表はい
イベント12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara, Japan
継続期間: 2011 6月 132011 6月 15

出版物シリーズ

名前Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011

Conference

Conference12th IAPR Conference on Machine Vision Applications, MVA 2011
国/地域Japan
CityNara
Period11/6/1311/6/15

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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