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Edge-Based Facial Feature Extraction using Adaptive Canny Operator Edge Detection

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dc.contributor.author Aung, Darli Myint
dc.date.accessioned 2019-11-15T04:10:43Z
dc.date.available 2019-11-15T04:10:43Z
dc.date.issued 2012-02-28
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2438
dc.description.abstract Facial feature extraction is an essential step in the face detection and facial expression recognition frameworks. To develop a better facial expression recognition system, a good feature extraction method is needed. In this paper, an efficient Facial Feature Extraction method for recognizing four different expressions such as neutral, happy, surprise and sad is presented. In this study, adaptive canny operator edge detection method is used to reduce the computational complexity and improved the accuracy of feature point location. To validate the performance of the proposed feature extraction, the generated features are classified using Maximum Correlation Classifier (MCC). 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. Our results also showed that the proposed feature extraction method is more efficient than Gabor wavelet edge detection method. en_US
dc.language.iso en_US en_US
dc.subject Adaptive Canny Operator Edge Detection en_US
dc.subject Facial Feature Extraction en_US
dc.subject Maximum Correlation Classifier en_US
dc.subject Gabar Wavelet edge detection method en_US
dc.title Edge-Based Facial Feature Extraction using Adaptive Canny Operator Edge Detection en_US
dc.type Article en_US


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