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Sasang Constitution Classification System by Morphological Feature Extraction of Facial Images
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 Title & Authors
Sasang Constitution Classification System by Morphological Feature Extraction of Facial Images
Lee, Hye-Lim; Cho, Jin-Soo;
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This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists` experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.
Sasang Constitution;Face Feature Extraction;Active Shape Model;Support Vector Machine;
 Cited by
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