Hybrid robust iris recognition approach using iris image pre‐processing, two‐dimensional gabor features and multi‐layer perceptron neural network/PSO

Abstract

Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for detecting crime in security systems because they offer attractive features such as stability and uniqueness. Although various methods have been proposed for this objective, feature shortcomings such as computational complexity, long run times, and high memory consumption remain. The current study proposes a novel human iris recognition approach based on a multi-layer perceptron NN and particle swarm optimisation (PSO) algorithms to train the network in order to increase generalisation performance. A combination of these algorithms was used as a classifier. A pre-processing step was performed on the iris images to improve the results and two-dimensional gabor kernel feature extraction was applied. The data was normalised, trained, and tested using the proposed method. A PSO algorithm was applied to train the NN for data classification. The experimental results show that the proposed method performs better than many other well-known techniques. The benchmark Chinese Academy of Science and Institute of Automation (CASIA)-iris V3 and Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) machine learning repository datasets were used for testing and comparison.

Enhanced Biometric Recognition for Secure Authentication using Iris Preprocessing and Hyper Elliptic Curve Cryptography

“…Their performance greatly varies due to light illumination and reflections. Neda et al [22] developed a hybrid robust iris recognition approach that uses 2D Gabor filter for feature extraction and neural network, PSO algorithm was employed to improve the generalization performance. Kalka et al uses different metrics for iris recognition such as iris occlusion, amount of reflection and gaze deviation.…”

Iris Recognition at-a-Distance by Means of Chronological MBO-Based DBN

“…Ahmadi N. and Akbarizadeh G [3] presented human iris acknowledgment strategy by mixing the Particle Swarm Optimization. calculation and multi-layer observation Neural Network (NN) so as to improve the speculation presentation.…”

“…At first, the image I is passed into the LOOP descriptor-1, which is denoted as I 1 . At the same time, the iris image I is passed to the Tetrolet transform (TT), to generate the image I 2 , which is further processed by the LOOP descriptor-2 to obtain the imageI 3 . The image I 1 is EXOR -ed with the imageI 3 and the resulted EXOR-ed image is further passed into the LOOP descriptor-3 to compute the image I 6 .…”

“…Recognizing the iris is the automatic biometric authentication tactic based on pattern recognition also statistical feature detection [2]. The biometric system can recognize the person by both behaviorally and physically, namely fingerprints, palm prints, face, signature, veins and voice [3]. Biometric system is a promising and constantly evolving technology used in the automatic system to identify the person"s identity efficiently and uniquely without the need to remember or carry anything, like Ids and passwords [4].…”

Feature extraction using sequential cumulative bin and overlap mean intensity for iris classification

“…The iris code proposed by Daugman contained bits 1 and 0 and was the earliest representation for iris textures. Over the years and in current practice [6][7][8][9][10][11][12][13][14][15][16], most of the methods generate the features from a two-dimensional domain of the iris image and then project it into real values or a binary representation. Finding local attributes for iris descriptors is common practice at the feature extraction level in iris recognition [17][18][19].…”