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Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm
Department of Information Technology, Kings Engineering College, Chennai, India
Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, India
- 1 Department of Information Technology, Kings Engineering College, Chennai, India
- 2 Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, India
Circuits and Systems·Volume 07 (2016)·Pages 1199–1206·Published 2 June 2016·DOI10.4236/cs.2016.78103
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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.
KeywordsMultimodal BiometricsScore Level Fusion ApproachNeural NetworkOptimization
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