skip to main content
Show Results with:

Pose-invariant face recognition with homography-based normalization

Ding, Changxing; Tao, Dacheng

Pattern recognition -- Elsevier Science -- Volume: 66 C; (pages 144-152) -- 2017

Online access

  • Title:
    Pose-invariant face recognition with homography-based normalization
  • Author: Ding, Changxing;
    Tao, Dacheng
  • Found In: Pattern recognition. Volume 66:Number C(2017); 201706; 144-152
  • Journal Title: Pattern recognition
  • Subjects: Pattern perception; Perception des structures Périodiques; Patroonherkenning; LCSH: Pattern perception; Dewey: 006.4
  • Rights: Licensed
  • Publication Details: Elsevier Science
  • Abstract: AbstractPose-invariant face recognition (PIFR) refers to the ability that recognizes face images with arbitrary pose variations. Among existing PIFR algorithms, pose normalization has been proved to be an effective approach which preserves texture fidelity, but usually depends on precise 3D face models or at high computational cost. In this paper, we propose an highly efficient PIFR algorithm that effectively handles the main challenges caused by pose variation. First, a dense grid of 3D facial landmarks are projected to each 2D face image, which enables feature extraction in an pose adaptive manner. Second, for the local patch around each landmark, an optimal warp is estimated based on homography to correct texture deformation caused by pose variations. The reconstructed frontal-view patches are then utilized for face recognition with traditional face descriptors. The homography-based normalization is highly efficient and the synthesized frontal face images are of high quality. Finally, we propose an effective approach for occlusion detection, which enables face recognition with visible patches only. Therefore, the proposed algorithm effectively handles the main challenges in PIFR. Experimental results on four popular face databases demonstrate that the propose approach performs well on both constrained and unconstrained environments.HighlightsWe propose a highly efficient and accurate pose normalization approach for pose-invariant face recognition.This is the first time that homography is utilized for face synthesis.The proposed approach covers the full range of pose variations within ±90° of yaw.The proposed approach outperforms existing methods on four popular face databases.
  • Identifier: Journal ISSN: 0031-3203
  • Publication Date: 2017
  • Physical Description: Electronic
  • Shelfmark(s): ELD Digital store
  • UIN: ETOCvdc_100041885120.0x000001

Searching Remote Databases, Please Wait