Illuminated facial preprocessing with wavelet local binary patterns histogram specification

Yi Zheng Goh, Michael Kah Ong Goh, Andrew B.J. Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Contemporary 2D face recognition is still a challenging work, especially when lighting varies. Thus, many works of resolving illumination variation in face recognition have been proposed, in the past decades. In this paper, we proposed Wavelet Local Binary Patterns Histogram Specification as a preprocessing technique for illuminated face recognition. Based on wavelet analysis, an illuminated facial image is decomposed into illumination and reflectance components. The illumination component that resides in the low spatial-frequency subband is first removed. Next, the reflectance component that resides in the high and middle spatial-frequency subband is then enhanced with local binary pattern histogram. This technique is promising in achieving better recognition performance on YaleB and CMU PIE face databases in comparison to the results that achieved by existing illumination invariant techniques.

Original languageEnglish
Title of host publicationICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing
DOIs
Publication statusPublished - 2009
Event7th International Conference on Information, Communications and Signal Processing, ICICS 2009 - Macau Fisherman's Wharf, Macao
Duration: 2009 Dec 82009 Dec 10

Publication series

NameICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing

Other

Other7th International Conference on Information, Communications and Signal Processing, ICICS 2009
Country/TerritoryMacao
CityMacau Fisherman's Wharf
Period09/12/809/12/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint

Dive into the research topics of 'Illuminated facial preprocessing with wavelet local binary patterns histogram specification'. Together they form a unique fingerprint.

Cite this