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Modeling and detection of wrinkles in aging human faces using marked point processes

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

Abstract

In this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable locations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the proposed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PublisherSpringer Verlag
Pages178-188
Number of pages11
EditionPART 2
ISBN (Print)9783642338670
DOIs
StatePublished - 2012
Externally publishedYes
EventComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Country/TerritoryItaly
CityFlorence
Period10/7/1210/13/12

Keywords

  • Markov Point Process
  • Modeling of wrinkles
  • Reversible Jump Markov Chain Monte Carlo
  • stochastic geometrical model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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