Classification of facial diagnosis gloss in Chinese medicine based on different algorithms

To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images.

To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)²PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method.

All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)²PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%.

A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.