Separability oriented preprocessing for illumination-insensitive face recognition

  • Authors:

  • Hu Han;Shiguang Shan;Xilin Chen;Shihong Lao;Wen Gao

  • Affiliations:

  • Institute of Computing Technology, CAS, Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Beijing, China,Department of Computer Science and Engineering, Michigan ...;Institute of Computing Technology, CAS, Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Beijing, China;Institute of Computing Technology, CAS, Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Beijing, China;Omron Social Solutions Co., LTD., Kyoto, Japan;Institute of Digital Media, Peking University, Beijing, China,Institute of Computing Technology, CAS, Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Beijing, C ...

  • Venue:
  • ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the last decade, some illumination preprocessing approaches were proposed to eliminate the lighting variation in face images for lighting-invariant face recognition. However, we find surprisingly that existing preprocessing methods were seldom modeled to directly enhance the separability of different faces, which should have been the essential goal. To address the issue, we propose to explicitly exploit maximizing separability of different subjects' faces as the preprocessing objective. With this in mind, a novel approach, named by us Separability Oriented Preprocessing (SOP), is proposed to enhance face images by maximizing the Fisher separability criterion in scale-space. Extensive experiments on both laboratory-controlled and real-world face databases using different recognition methods show the effectiveness of the proposed approach.