Earticle

초록

영어

In this paper, face recognition using the most representative SIFT images is presented. It is based on obtaining the SIFT (SCALE INVARIANT FEATURE TRANSFORM) features in different regions of each training image. Those regions were obtained using the K-means clustering algorithm applied on the key-points obtained from the SIFT algorithm. Based on these features, an algorithm which will get the most representative images of each face is presented. In the test phase, an unknown face image is recognized according to those representative images. In order to show its effectiveness this algorithm is compared to other SIFT algorithms and to the LDP algorithm for different databases.

목차

Abstract
 1. Introduction
 2. Scale Invariant Feature Tr
  2.1. Construction a Scale Space
  2.2. Laplacian of Gaussian Calculation
  2.3. Finding Key-points
  2.4. Eliminating Edges and Low Contrast Regions
  2.5. Assigning an Orientation to the Key-points
  2.6. SIFT Features Generation
 3. Training Phase
  3.1. The k-regions Formation
  3.2. Obtaining the most Representative Images
 4. Test Phase
 5. Experimental Results
 6. Conclusion
 References

키워드

저자

• Issam Dagher [ University of Balamand Department of Computer Engineering ]
• Nour El Sallak [ University of Balamand Department of Computer Engineering ]
• Hani Hazim [ University of Balamand Department of Computer Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605