Normalization of face illumination based on large-and small-scale features

Xiaohua Xie*, Wei Shi Zheng, Jianhuang Lai, Pong Chi YUEN, Ching Y. Suen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)

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.

Original languageEnglish
Article number5658185
Pages (from-to)1807-1821
Number of pages15
JournalIEEE Transactions on Image Processing
Volume20
Issue number7
DOIs
Publication statusPublished - Jul 2011

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Face recognition
  • illumination normalization
  • visual compensation

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