Earticle

초록

영어

This paper deals with Face recognition using Single training sample which is a new challenging problem in machine vision. In the proposed method, first four different representation of face are generated using Gabor filters which vary in angle. Then a Baseclassifier is assigned for each of them and also for original image. Finally EMV technique combines the Base-classifiers. EMV behaves like MV but chooses the vote of the Baseclassifier assigned to original image as winner class when there is multiple winner class. Experimental results on ORL face dataset, show an improvement about 2%, 4% and 5% than 2DPCA, (PC)2A and PCA respectively.

목차

Abstract
 1. Introduction
 2. Outline of Two Dimensional Principle Component Analysis
  2.1 Basic steps of 2DPCA
 3. Outline of Gabor filter
 4. Proposed method
  4.1 Applying a set of Gabor filters on face images to create a new representation of them
  4.2 Extracting the informative features from samples using 2DPCA transform
  4.3 Classifying the face images using a two-stage classifier system
 5. Experimental results
 6. Conclusion
 References

키워드

저자

• Reza Ebrahimpour [ Shahid Rajaee Teacher training University, Tehran, Iran ]
• Masoom Nazari [ Shahid Rajaee Teacher training University, Tehran, Iran ]
• Mehdi Azizi [ Shahid Rajaee Teacher training University, Tehran, Iran ]
• Ali Amiri [ Shahid Rajaee Teacher training University, Tehran, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605