Earticle

초록

영어

Most commonly used face recognition algorithms are based on extraction of global features using eigenvalue decomposition of some relational matrix of image intensity values. Real time face recognition applications require a computationally efficient algorithm for eigenvalues generation. Fast principal component analysis (FPCA) is an algorithm for efficient generation of eigenvalues which improves the computational efficiency to O(n2) as compared to normal decomposition method which gives the solution in O(n3) time. In FPCA however, nonconvergence state can be resulted for high resolution images because in this case the number of Grams-Schmidt (GS) iterations for orthonormalization convergence may exceed the maximum limit. To overcome this problem we present a modified FPCA algorithm to generate eigenvalues for images including those at high resolution. An overall efficient face recognition scheme has also been proposed using the generated eigenvalues, which can work satisfactorily under varying image resolutions. The validity of the proposed system has been checked by varying the feature vectors and the training sets. The developed technique provides an efficient and a low error rate solution for high speed image recognition systems.

목차

Abstract
 1. Introduction
 2. Limitations of FPCA
 3. Modification in FPCA algorithm
  3.1. AFPCA Classifier Algorithm
 4. Results and Discussion
 5. Conclusion
 6. References

저자

• I. Sajid, M.M. Ahmed [ Department of Electronic Engineering Mohammad Ali Jinnah University (MAJU) ]
• I. Taj [ Department of Electronic Engineering Mohammad Ali Jinnah University (MAJU) ]

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