Normalization of Face Illumination Based on Large-and Small-Scale Features
- 6 December 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 20 (7), 1807-1821
- https://doi.org/10.1109/tip.2010.2097270
Abstract
A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.Keywords
This publication has 38 references indexed in Scilit:
- Independent Gabor Analysis of Multiscale Total Variation-Based Quotient ImageIEEE Signal Processing Letters, 2008
- Shadow compensation in 2D images for face recognitionPattern Recognition, 2007
- Component-based LDA face description for image retrieval and MPEG-7 standardisationImage and Vision Computing, 2005
- Face recognition based on fitting a 3D morphable modelIeee Transactions On Pattern Analysis and Machine Intelligence, 2003
- The CMU Pose, Illumination, and Expression (PIE) databasePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Illumination normalization for robust face recognition against varying lighting conditionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A signal-processing framework for inverse renderingPublished by Association for Computing Machinery (ACM) ,2001
- The FERET evaluation methodology for face-recognition algorithmsIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Objective picture quality scale (PQS) for image codingIEEE Transactions on Communications, 1998
- Face recognition: the problem of compensating for changes in illumination directionIeee Transactions On Pattern Analysis and Machine Intelligence, 1997