期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：85
期号：2
出版社：Journal of Theoretical and Applied
摘要：This paper presents an algorithm for detecting smooth texture in facial images which is prone to unnatural contrast enhancement. The algorithm consists of texture analysis and machine learning algorithm. Wavelet decomposition is used for texture analysis. Smooth texture tends to have small variance among the wavelet coefficients within the same scale. This paper proposes to divide image into 32�32 sub-image with overlapping of 16 pixels, then perform wavelet decomposition with 5 scales. The final feature is a 5 dimensional vector consists of the variance of the wavelet coefficients from each of the 5 scales. Support Vector Machine (SVM) is used for feature classification. The SVM classifier was trained using 468 samples consist of samples from skin areas (smooth texture) and non-smooth area (eye and nose) of 78 test images. The performance of the classifier was evaluated using k-fold cross validation with k range from 2 to 10. The performance was excellent with the average accuracy for each value of k above 95%. The performance was also very consistent across different set of test images with standard deviation range from 1% ~ 4%.
