Facial image reconstruction by SVDD-based pattern de-noising

Jooyoung Park, Daesung Kang, James T. Kwok, Sang Woong Lee, Bon Woo Hwang, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. In this paper, we consider the problem of reconstructing facial images from the partially damaged ones, and propose to use the SVDD-based de-noising for the reconstruction. In the proposed method, we deal with the shape and texture information separately. We first solve the SVDD problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the SVDD problem. Next, for each damaged input facial image, we project its feature vector onto the decision boundary of the SVDD ball so that it can be tailored enough to belong to the normal region. Finally, we obtain the image of the reconstructed face by obtaining the pre-image of the projection, and then further processing with its shape and texture information. The applicability of the proposed method is illustrated via some experiments dealing with damaged facial images.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Number of pages7
Publication statusPublished - 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 2006 Jan 52006 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Biometrics, ICB 2006
CityHong Kong

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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