TY - GEN
T1 - Contrast compensation for back-lit and front-lit color face images via fuzzy logic classification and image illumination analysis
AU - Tsai, Chun Ming
AU - Yeh, Zong M.U.
AU - Wang, Yuan Fang
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Color face images
KW - Contrast compensation
KW - Fuzzy logic classification
KW - Image illumination analysis
KW - Parameter-free
UR - http://www.scopus.com/inward/record.url?scp=57849115396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57849115396&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2008.4621022
DO - 10.1109/ICMLC.2008.4621022
M3 - Conference contribution
AN - SCOPUS:57849115396
SN - 9781424420964
T3 - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
SP - 3563
EP - 3568
BT - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
T2 - 7th International Conference on Machine Learning and Cybernetics, ICMLC
Y2 - 12 July 2008 through 15 July 2008
ER -