@article{MAKHILLIJSC20138321135,
    title = {Fuzzy Rule Based Neuro-Genetic Approach for Fingerprint Recognition},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {154-162},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.154.162},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.154.162},
    author = {M. and},
    keywords = {Fuzzy rules,neuro-genetic algorithm,PSNR,MSE,EER},
    abstract = {In this study, the accuracy of finger print recognition problem 
  is been addressed. As per the literature the Back Propagation Network (BPN) 
  for fingerprint recognition has resulted in inconsistent with unpredictable 
  performance. This research has proposed the soft computing tool to images to 
  overcome the low recognition rate and the low accuracy in fingerprint identification. 
  Fuzzy logic is worn to eliminate the false minutiae from the fingerprint. Genetic 
  algorithm has been incorporated to optimize the weights of neural network and 
  the accuracy in the recognition process has been improved. The proposed method 
  is implemented on the FVC 2004 DB1 database. The Laplacian based Pyramidal Model 
  has strongly supported in fingerprint enhancement process which has increased 
  the Peak Signal to Noise Ratio (PSNR) and decreased the Mean Square Error (MSE). 
  The results have proven that the false minutiae have been eliminated by applying 
  fuzzy rules and also the Equal Error Rate (EER) has been reduced. The increase 
  in the recognition accuracy moreover in turn has reduced the training and the 
  testing time.}
    }