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Automatic Denoising of 2D Color Face Images Using Recursive PCA Reconstruction

  • Hyun Park
  • Young Shik Moon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noise components on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following six steps: training of canonical eigenface space using PCA, automatic extraction of facial features using active appearance model and alignment of the input face to mean shape, reconstruction of an initial noise free face, relighting of reconstructed face using a bilateral filter, extraction of noise regions using the variances of skin color of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denoising method maintains the structural characteristics of input faces, while efficiently removing noise components with complex colors.

Keywords

Impulse Noise Bilateral Filter Denoising Method Reconstructed Face Input Face 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyun Park
    • 1
  • Young Shik Moon
    • 1
  1. 1.Department of Computer Science and EngineeringHanyang UniversityKyunggi-DoKorea

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