Contrast Compensation for Back-lit and Front-lit Color Face Image via Fuzzy Logic Classification and Image Illumination Analysis

uTaipei > College of Science > Department of Computer Science > Proceedings > Item 987654321/15854

Please use this identifier to cite or link to this item: http://utaipeir.lib.utaipei.edu.tw/dspace/handle/987654321/15854

Title:	Contrast Compensation for Back-lit and Front-lit Color Face Image via Fuzzy Logic Classification and Image Illumination Analysis
Authors:	Tsai, C. M.;蔡俊明;Yeh, Z. M.;Wang, Y. F.
Contributors:	臺北市立教育大學資訊科學系
Keywords:	Contrast compensation;Fuzzy logic classification;Image illumination analysis;Parameter-free;Color face images
Date:	2008
Issue Date:	2017-07-25 09:57:37 (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 color face images. This method includes: RGB color space is transformed to YIQ color space. Fuzzy logic is used to classify the color images into back-lit, normal-lit, and front-lit categories. Image illumination analysis is used to analyze the image distribution. The input image is compensated by piecewise linear based compensation method. Finally, the compensation image is transformed back to RGB color space. This novel compensation method is automatic and parameter-free. Our experiments included back-lit and front-lit images. Experiment results show that the performance of the proposed method is better than other available methods in visual perception measurements.
Appears in Collections:	[Department of Computer Science] Proceedings

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