A modified adjacent pixel intensity difference quantization method for face recognition

Feifei Lee, Koji Kotani, Qiu Chen, Tadahiro Ohmi

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

Abstract

We have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram previously. In this paper, we present a modified quantization method to improve recognition performance. 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. Experimental results show maximum average recognition rate of 97.2% for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge.

Original languageEnglish
Title of host publication2009 2nd International Conference on Future Information Technology and Management Engineering, FITME 2009
Pages533-536
Number of pages4
DOIs
Publication statusPublished - 2009 Dec 1
Event2009 2nd International Conference on Future Information Technology and Management Engineering, FITME 2009 - Sanya, China
Duration: 2009 Dec 132009 Dec 14

Publication series

Name2009 2nd International Conference on Future Information Technology and Management Engineering, FITME 2009

Other

Other2009 2nd International Conference on Future Information Technology and Management Engineering, FITME 2009
CountryChina
CitySanya
Period09/12/1309/12/14

Keywords

  • Adjacent pixel intensity difference quantization (APIDQ)
  • Face recognition
  • Histogram

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Information Systems
  • Information Systems and Management

Fingerprint Dive into the research topics of 'A modified adjacent pixel intensity difference quantization method for face recognition'. Together they form a unique fingerprint.

Cite this