Illumination invariant face recognition

Dang Hui Liu, Kin Man Lam, Lan Sun Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

72 Citations (Scopus)

Abstract

The appearance of a face will vary drastically when the illumination changes. Variations in lighting conditions make face recognition an even more challenging and difficult task. In this paper, we propose a novel approach to handle the illumination problem. Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image between the face image and a reference face image, both of which are blurred by a Gaussian filter. An iterative algorithm is then used to update the reference image, which is reconstructed from the restored image by means of principal component analysis (PCA), in order to obtain a visually better restored image. Image processing techniques are also used to improve the quality of the restored image. To evaluate the performance of our algorithm, restored images with frontal illumination are used for face recognition by means of PCA. Experimental results demonstrate that face recognition using our method can achieve a higher recognition rate based on the Yale B database and the Yale database. Our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions, (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations, and (3) it does not need to detect accurate positions of some facial feature points or to warp the image for alignment, etc.
Original languageEnglish
Pages (from-to)1705-1716
Number of pages12
JournalPattern Recognition
Volume38
Issue number10
DOIs
Publication statusPublished - 1 Oct 2005

Keywords

  • Adaptive filter
  • Edge extraction
  • Lambertian reflectance
  • Optimal threshold segmentation
  • Principal component analysis
  • Ratio image

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this