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Grabowski, K., Sankowski, W., et al. (2006) Iris Recognition Algorithm Optimized for Hardware Implementation. IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Toronto, 28-29 September 2006, 1-5.

has been cited by the following article:

• TITLE: Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm

  AUTHORS: E. Sujatha, A. Chilambuchelvan

  KEYWORDS: Multimodal Biometrics, Score Level Fusion Approach, Neural Network, Optimization

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 6, 2016

  ABSTRACT: A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.