A robust face recognition algorithm using Markov Stationary Features and adjacent pixel intensity difference quantization histogram

Feifei Lee, Koji Kotani, Qiu Chen, Tadahiro Ohmi

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

1 Citation (Scopus)

Abstract

In this paper, we present a robust face recognition algorithm using adjacent pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF). Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of r-θ plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with MSF so as to add spatial structure information to histogram. Experimental results show maximum average recognition rate of 97.16% is obtained for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011
Pages334-339
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011 - Dijon, France
Duration: 2011 Nov 282011 Dec 1

Publication series

NameProceedings - 7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011

Other

Other7th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2011
Country/TerritoryFrance
CityDijon
Period11/11/2811/12/1

Keywords

  • Adjacent pixel intensity difference quantization (APIDQ)
  • Face recognition
  • Histogram feature
  • Markov stationary features (MSF)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint

Dive into the research topics of 'A robust face recognition algorithm using Markov Stationary Features and adjacent pixel intensity difference quantization histogram'. Together they form a unique fingerprint.

Cite this