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

Rosdiyana Samad, Hideyuki Sawada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
Pages430-433
Number of pages4
Publication statusPublished - 2011
Externally publishedYes
Event12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara
Duration: 2011 Jun 132011 Jun 15

Other

Other12th IAPR Conference on Machine Vision Applications, MVA 2011
CityNara
Period11/6/1311/6/15

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Samad, R., & Sawada, H. (2011). Edge-based facial feature extraction using Gabor wavelet and convolution filters. In Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011 (pp. 430-433)