Loading...

Face recognition using boosted regularized linear discriminant analysis

Baseri Salehi, N ; Sharif University of Technology | 2010

874 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICCMS.2010.318
  3. Publisher: 2010
  4. Abstract:
  5. Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper, we have proposed the boosting method for face recognition (FR) that improves the linear discriminant analysis (LDA)-based technique. The improvement is achieved by incorporating the regularized LDA (R-LDA) technique into the boosting framework. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. The AdaBoost technique is utilized within this framework to generalize a set of simple FR subproblems and their corresponding LDA solutions and combines the results from the multiple, relatively weak, LDA solutions to form a strong solution. The comparative experimental result on FERET database demonstrates that the proposed boosting method achieves more accurate results over the individual algorithms
  6. Keywords:
  7. AdaBoost ; Boosting ; Face recognition ; Regularized LDA ; Small-sample-size problem ; AdaBoost ; Boosting method ; FERET database ; Fisher's discriminant ; General method ; Linear discriminant analysis ; Small sample size problems ; Strong solution ; Sub-problems ; Adaptive boosting ; Computer simulation ; Discriminant analysis ; Face recognition
  8. Source: ICCMS 2010 - 2010 International Conference on Computer Modeling and Simulation, 22 January 2010 through 24 January 2010, Sanya ; Volume 2 , 2010 , Pages 89-93 ; 9780769539416 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5421297