Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 36; no. 2; pp. 458 - 466
• Main Authors: Chen, W., Meng Joo Er, Shiqian Wu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2006
• Subjects: Adaptive equalizers; Algorithms; Artificial Intelligence; Cybernetics; Discrete cosine transform; Discrete cosine transforms; Erbium; Face - anatomy & histology; Face recognition; Histograms; Humans; Illumination; illumination normalization; Image databases; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Lighting; logarithm transform; Logarithms; Pattern Recognition, Automated - methods; Photography - methods; Photometry - methods; Reproducibility of Results; Robustness; Sensitivity and Specificity; Shape; Signal Processing, Computer-Assisted
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2005.857353

This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.