@article{MAKHILLIJSC20149421211,
    title = {Multimodal Biometric Authentication System Based Performance Scrutiny},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {4},
    pages = {246-254},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.246.254},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.246.254},
    author = {R.,A. Shajin and},
    keywords = {Biometric identification system,Fisher’s Linear Discriminant Methods (FLD),Multibiometric System,Principal Component Analysis (PCA),rank-level fusion},
    abstract = {In many real-world applications, Unimodal Biometric Systems 
  often face significant limitations due to sensitivity to noise, intraclass variability, 
  data quality, non-universality and other factors. Multibiometric Systems seek 
  to alleviate some of these problems by providing multiple pieces of evidence 
  of the same identity. This study presents an effective fusion scheme that combines 
  information presented by multiple domain experts based on the Rank-Level Fusion 
  Integration Method. The developed Multimodal Biometric System possesses a number 
  of unique qualities, starting from utilizing principal component analysis and 
  Fisher’s Linear Discriminant 
  Methods for individual matchers (face, iris and fingerprint) identity authentication 
  and utilizing the Novel Rank-Level Fusion Method in order to consolidate the 
  results obtained from different biometric matchers.The results indicate that 
  fusion of individual modalities can improve the overall performance of the Biometric 
  System, even in the presence of low quality data.}
    }