ROBUST SECURITY USING NEURAL NETWORKS ALGORITHMS FOR IRIS RECOGNITION
Author(s):
NIGILA S, Dr.G.Gandhimathi
Keywords:
Canny Edge Detection; Circular Hough transform; Normalization; Particle swarm optimization; Gravitational search algorithm; Principle component analysis
Abstract
This paper introduces an iris classification system using FFNNGSA and FFNNPSO. The use of both methods has not been done before in iris recognition. This iris identification system consists of localization of the iris region, normalization, feature extraction and then classification as a final stage. A Canny Edge Detection scheme and a Circular Hough Transform are used to detect the iris boundaries. After that the extracted IRIS region is normalized using Daugman rubber sheet model. Next, Haar wavelet transform is used for extracting features from the normalized iris region then the feature matrix is reduced using the principle component analysis (PCA). Finally, both particle swarm optimization (PSO) and gravitational search algorithm (GSA) are used for training a forward neural network to get the optimum weights and biases that give minimum error and higher recognition rate for the FFNN in iris classification. These optimization techniques used in classification strengthen the work. The results showed that training the feedforward neural network by GSA is better than training it by PSO in an iris recognition system.
Article Details
Unique Paper ID: 146598

Publication Volume & Issue: Volume 5, Issue 1

Page(s): 159 - 165
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