A Face Recognition Algorithm Based on Improved Contourlet Transform and Principle Component Analysis

Jinhua Zhang, Daniel Scholten

Abstract


As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition.


Keywords


contourlet transform, principal component analysis, face recognition

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v14i2A.4371

Article Metrics

Abstract view : 129 times
PDF - 100 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 Universitas Ahmad Dahlan

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View TELKOMNIKA Stats