CONTRAST COMPENSATION FOR BACK-LIT AND FRONT-LIT COLOR FACE IMAGES VIA FUZZY LOGIC CLASSIFICATION AND IMAGE ILLUMINATION ANALYSIS
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.
Contrast compensation Fuzzy logic classification Image illumination analysis Parameter-free Color face images
CHUN-MING TSAI ZONG-MU YEH YUAN-FANG WANG
Department of Computer Science, Taipei Municipal University of Education, Taipei 100, Taiwan, ROC Department of Mechatronic Technology, National Taiwan Normal University, Taipei 106, Taiwan, ROC Department of Computer Science, University of California, Santa Barbara, CA93106, USA
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3563-3568
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）