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Title: Detection of smooth texture in facial images for the evaluation of unnatural contrast enhancement
Authors: Ismail, N.H.B. 
Chen, S.-D. 
Issue Date: 2016
Abstract: 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%. © 2005 - 2016 JATIT & LLS. All rights reserved.
Appears in Collections:COGS Scholarly Publication

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