Contrast Compensation by Fuzzy Classification and Image Illumination Analysis for Back-lit and Front-lit Color Face Images

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Title:	Contrast Compensation by Fuzzy Classification and Image Illumination Analysis for Back-lit and Front-lit Color Face Images
Authors:	Tsai, C. M.;蔡俊明;Yeh, Z. M.
Contributors:	臺北市立教育大學資訊科學系
Date:	2010-08
Issue Date:	2017-07-25 09:58:38 (UTC+8)
Abstract:	Conventional contrast enhancement methods have two shortcomings. First, most of them do not produce satisfactory enhancement results for face images with back-lit or front-lit. Second, most of them need transformation functions and parameters which are specified manually. Thus, this paper proposes an automatic and parameter-free contrast compensation algorithm for skin detection in color face images. This method includes following steps: First, RGB color space is transformed to YIQ color space. Second, fuzzy logic is used to classify the color images into three categories: back-lit, normal-lit, and front-lit. Third, image illumination analysis is used to analyze the image distribution. Fourth, the input image is compensated by piecewise linear based enhancement method. Finally, the compensation image is transformed back to RGB color space. Our experiments included various color and gray face images. Experiment results show that the performance of the proposed compensation method is better than other available methods in skin detection and visual perception measurements
Relation:	IEEE Transactions on Consumer Electronics， Vol. 56， No. 3， pp. 1570-1578
Appears in Collections:	[Department of Computer Science] Periodical Articles

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