A comparative study of baseline algorithms of face recognition

Zahid Mehmood, Tauseef Ali, Shahid Khattak, Samee U. Khan

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

10 Citations (Scopus)
266 Downloads (Pure)

Abstract

In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.
Original languageEnglish
Title of host publication12th International Conference on Frontiers of Information Technology, FIT 2014
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages263-268
Number of pages6
ISBN (Electronic)978-1-4799-7505-1
ISBN (Print)978-1-4799-7504-4
DOIs
Publication statusPublished - Dec 2014
Event12th International Conference on Frontiers of Information Technology, FIT 2014 - Islamabad, Pakistan
Duration: 17 Dec 201419 Dec 2014

Conference

Conference12th International Conference on Frontiers of Information Technology, FIT 2014
Period17/12/1419/12/14
Other17-19 December 2014

Keywords

  • SCS-Safety
  • METIS-312728
  • IR-97324
  • EWI-26323

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