Combining local binary pattern and principal component analysis on T-zone face area for face recognition

Md. Jan Nordin, Abdul Aziz K Abdul Hamid

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

6 Citations (Scopus)

Abstract

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a significant facial region, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
Pages25-30
Number of pages6
Volume1
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 - Putrajaya
Duration: 28 Jun 201129 Jun 2011

Other

Other2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
CityPutrajaya
Period28/6/1129/6/11

Fingerprint

Face recognition
Principal component analysis
Research laboratories
Feature extraction
Experiments

Keywords

  • Face Recognition
  • Local Binary Pattern
  • Principle Component Analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Nordin, M. J., & Hamid, A. A. K. A. (2011). Combining local binary pattern and principal component analysis on T-zone face area for face recognition. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 (Vol. 1, pp. 25-30). [5976906] https://doi.org/10.1109/ICPAIR.2011.5976906

Combining local binary pattern and principal component analysis on T-zone face area for face recognition. / Nordin, Md. Jan; Hamid, Abdul Aziz K Abdul.

Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. p. 25-30 5976906.

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

Nordin, MJ & Hamid, AAKA 2011, Combining local binary pattern and principal component analysis on T-zone face area for face recognition. in Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. vol. 1, 5976906, pp. 25-30, 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011, Putrajaya, 28/6/11. https://doi.org/10.1109/ICPAIR.2011.5976906
Nordin MJ, Hamid AAKA. Combining local binary pattern and principal component analysis on T-zone face area for face recognition. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1. 2011. p. 25-30. 5976906 https://doi.org/10.1109/ICPAIR.2011.5976906
Nordin, Md. Jan ; Hamid, Abdul Aziz K Abdul. / Combining local binary pattern and principal component analysis on T-zone face area for face recognition. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. pp. 25-30
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