Fast Template matching method based on optimized metrics for face localization

Nadir Nourain Dawoud*, Brahim Belhaouari Samir, Josefina Janier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Recently, Template matching approach has been widely used for face localization problem. Normalized Cross-correlation (NCC) is a measurement method normally utilized to compute the similarity matching between the templates and the rectangular blocks of the input image to locate the face position. However, the NCC metric is always suffering to locate the face especially in the images with illumination variations. In this paper we proposed a fast template matching technique based on Optimized similarity measurement metrics namely: Sum of Absolute Difference (OSAD) and Sum of Square Difference (SSD) to overcome the drawback of NCC. Our results show the highest performance of OSAD compared with other measurements and the improvement of OSSD comparing with SSD as well. Two sets of faces namely Yale Dataset and MIT-CBCL Dataset were used to evaluate our technique with success localization accuracy up to 100%.

Original languageEnglish
Title of host publicationInternational MultiConference of Engineers and Computer Scientists, IMECS 2012
PublisherNewswood Limited
Pages726-729
Number of pages4
ISBN (Print)9789881925114
Publication statusPublished - 2012
Externally publishedYes
Event2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, Hong Kong
Duration: 14 Mar 201216 Mar 2012

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2195
ISSN (Print)2078-0958

Conference

Conference2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012
Country/TerritoryHong Kong
CityKowloon
Period14/03/1216/03/12

Keywords

  • Face localization
  • Similarity measurements
  • Sum of Absolute Difference
  • Template matching

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