Robust face recognition using subface hidden Markov models

Shih Ming Huang, Jar-Ferr Yang, Shih Cheng Chang

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

2 Citations (Scopus)

Abstract

In this paper, a novel face recognition system using partitioned hidden Markov models is introduced to deal with partial occlusion problems. The proposed subface based system divides the face into forehead, eyes, nose, mouth, and chin, five subregions, which are characterized by five separated subface HMMs such that we can reconfigure these subface HMMs to achieve partially occluded face recognition. Moreover, we also suggested a facial grammar network to manipulate these subface HMMs to form various composite face HMMs. The Viterbi algorithm is used to estimate the likelihood score to perform face recognition with maximum likelihood criteria. Experiments are carried out on George Tech (GT) and AR facial databases. Experimental results reveal that the proposed system outperforms the embedded HMM (EHMM) and demonstrates promising abilities against partial occlusions and robustness against different facial expressions and illumination variations.

Original languageEnglish
Title of host publicationISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems
Subtitle of host publicationNano-Bio Circuit Fabrics and Systems
Pages1547-1550
Number of pages4
DOIs
Publication statusPublished - 2010 Aug 31
Event2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010 - Paris, France
Duration: 2010 May 302010 Jun 2

Publication series

NameISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems

Other

Other2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010
CountryFrance
CityParis
Period10-05-3010-06-02

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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