Abstract
Skin condition closely reflects human health, and skin roughness is one of the factors to analyze skin condition. This paper presents a classification algorithm of skin roughness based on improved support vector machine. The improved gray co-occurrence matrix, Tamura texture feature, fractal dimension, Gabor feature and other 24 properties affecting skin roughness were extracted by using human skin samples captured by visible light as data sources. Genetic algorithm was used to optimize the feature space, and the most important feature combinations were selected according to the fitness values. In order to improve the performance of classifier, the improved particle swarm optimization algorithm is used to optimize the penalty factor C and radial kernel function coefficient Gamma of support vector machine. The experiment shows that the model established in this paper has an obvious effect on the classification of skin roughness.
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