Improved nuisance attribute projection for face recognition

Ariel Yifrach, Eitan Novoselsky, Yosef A. Solewicz, Yitzhak Yitzhaky

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The illumination variation is one of the well-known problems in face recognition under uncontrolled environments. Several techniques have been presented in the literature to cope up with this problem. Lately, a technique known as Nuisance Attribute Projection (NAP), originally developed for the speaker recognition field was introduced to image processing in order to compensate for luminance artifacts. This paper extends and improves the earlier work by exploring efficient methodologies for using NAP for face recognition under varied illumination conditions. In particular, we propose a modified NAP formulation and show that NAP training can be simplified for face recognition. Additionally, we suggested a compact framework merging between NAP compensation and eigenface recognition. A series of experiments using the extended YaleB database, and a cross-validation using the PIE CMU and the Oulo databases are performed to validate our proposals.

Original languageEnglish
Pages (from-to)69-78
Number of pages10
JournalPattern Analysis and Applications
Issue number1
StatePublished - 1 Feb 2016


  • Face recognition
  • Nuisance Attribute Projection
  • Principle Component Analysis
  • Support Vectors Machine

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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