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Model-based multi-view face construction and recognition in videos

  • Chao Wang*
  • , Yunhong Wang
  • , Zhaoxiang Zhang
  • , Yiding Wang
  • *Corresponding author for this work
  • Beihang University
  • North China University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Model-based face construction and recognition in videos is a fundamental topic in image processing and video representation, while analysis faces across multiple views is more challenging than that from a fixed view because of the severe non-linearity caused by rotation in depth, self-occlusion, self-shading and illumination. In this paper, a novel method is presented to model and recognize multi-view faces in video sequences. Firstly, we design a multi-view face model to extract the face feature points. Secondly, a hybrid tracking method integrated optical flow with mean shift is proposed to estimate the face posture. Then, by using faces' paths in different view and feature points obtained from models, a multi-view face map is synthesized by reconstruction and stitching the paths together. Finally, recognition experiments are conducted to evaluate the performance of our proposed approach.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Applications - 8th International Conference, ICIC 2012, Proceedings
Pages280-287
Number of pages8
DOIs
StatePublished - 2012
Event8th International Conference on Intelligent Computing Theories and Applications, ICIC 2012 - Huangshan, China
Duration: 25 Jul 2012 → 29 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7390 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Intelligent Computing Theories and Applications, ICIC 2012
Country/TerritoryChina
CityHuangshan
Period25/07/12 → 29/07/12

Keywords

  • Active Appearance Model
  • Face recognition
  • Image stitching
  • Video-based face recognition

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