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Cited by 560

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

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

Chen, W., Meng Joo Er, Shiqian Wu·2006·IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)

Abstract

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.

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Metadata

Authors
Chen, W., Er, M. J., & Wu, S.
Venue
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
Published
2006-04
Volume
36
Issue
2
Pages
458-466
ISSN
1083-4419 (ISSN-L)
OpenAlex
W2098951409
Cited by
560
References
22
FWCI
16.47
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. https://app.cochat.ai/papers/10.1109/tsmcb.2005.857353
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Chen, W., Er, M. J., & Wu, S. (2006). Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/tsmcb.2005.857353

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.