@article{MAKHILLIJSC20138321146,
    title = {ORICS Based Kernel Discriminant Analysis},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {223-230},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.223.230},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.223.230},
    author = {R. Mathu,S. Kumar and},
    keywords = {Face recognition,multiple discriminant analysis,optimal random image component selection,kernel discriminant analysis,recognition accuracy},
    abstract = {An optimal random image component selection algorithm using 
  greedy approach is presented in this study. The proposed algorithm when evaluated 
  with hierarchical ensemble classifier has an enhanced recognition rate with 
  large variations in illumination, pose and facial expression. In the proposed 
  technique, features are extracted from the optimal random image components which 
  are then projected to the multiple discriminant analysis and kernel discriminant 
  analysis subspace for solving linear and non-linear problems. The number of 
  local image components is varied from 1-40 and by means of optimality checking, 
  it is observed that at least 10-20 image components are sufficient for reasonable 
  recognition. The FERET and ORL face datasets were used to generate the results. 
  The method has achieved 99.44 and 100% recognition accuracy and 82.5 and 99.64% 
  recognition accuracy on FERET and ORL datasets for 30% training, respectively. 
  This is a considerably improved performance than one attainable with other standard 
  methodologies described in the literature.}
    }