Multiscale morphology based illumination normalization with enhanced local textures for face recognition

Sanghun Lee, Chulhee Lee

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


A key challenge of face recognition is to obtain illumination invariant face images while preserving the discriminative features. The locations and shapes of small-scale features (e.g. eyebrows, eyes, nostrils, a mouth, etc.) are usually treated as key features for face recognition. However, it has also been observed that the local texture information of facial regions contains intrinsic facial features and needs to be enhanced to improve performance. To compensate for the illumination effects that appeared while extracting both the small-scale features and the texture information, we used multiscale morphological techniques. We used a generalized dynamic morphological quotient image (GDMQI) method based on Retinex theory and multiscale morphological closing to solve the artifact problem discussed in previous works. The proposed method consisted of two main steps: (i) illumination estimation and (ii) texture enhancement. The proposed method showed improved performance when using the CMU PIE, AR and Extended Yale-B databases.

Original languageEnglish
Pages (from-to)347-357
Number of pages11
JournalExpert Systems with Applications
Publication statusPublished - 2016 Nov 15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A01006421).

Publisher Copyright:
© 2016 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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