2 research outputs found
An improved Genetic Optimized Neural Network for Multimodal Biometrics
23-30In
this paper, a novel classification technique for multimodal biometric system
based on fingerprint and palmprint is proposed. The problems faced in unimodal
biometric system such as noisy data, intra class variations, restricted degrees
of freedom, non-universality, spoof attacks, and unacceptable error rates are
overcome in multimodal biometric system by integrating the evidence presented
by multiple traits. It is proposed to fuse the features of the fingerprint with
palmprint images. Features are
extracted
using Gabor filter and Discrete Cosine Transform (DCT). The extracted feature
vectors were classified using an improved Partial Recurrent Neural Network with
genetic optimization. The proposed Momentum Optimized Genetic Partial
Recurrent Neural Network (MOG-PRNN) was evaluated
using a publicly available dataset and features obtained from live dataset. The
experimental results obtained show an average classification accuracy of 98.6%
with different datasets.</span