An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network

Iris recognition has been shown to be very accurate for human identification. In this chapter, an efficient and automatic iris recognition system using Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into a rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results show that the system has good performance.

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A MULTI-BIOMETRIC IRIS RECOGNITION SYSTEM USING CONVOLUTION NEURAL NETWORK

i-manager’s Journal on Pattern Recognition ◽

10.26634/jpr.7.1.17405 ◽

2020 ◽

Vol 7 (1) ◽

pp. 1

Author(s):

SUMA JAYA ◽

OGURI SAM ◽

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Keyword(s):

Neural Network ◽

Iris Recognition ◽

Recognition System ◽

Convolution Neural Network

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Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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Neural network based Iris recognition system using Haralick features

2011 3rd International Conference on Electronics Computer Technology ◽

10.1109/icectech.2011.5941793 ◽

2011 ◽

Cited By ~ 9

Author(s):

R. Meenakshi Sundaram ◽

Bibhas Chandra Dhara

Keyword(s):

Neural Network ◽

Iris Recognition ◽

Recognition System ◽

Haralick Features

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Multi-Modal Iris Recognition System based on Convolution Neural Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j8911.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 798-803

Keyword(s):

Neural Network ◽

Confidence Level ◽

Iris Recognition ◽

Recognition System ◽

Data Sets ◽

Fusion Method ◽

Softmax Classifier ◽

Discriminative Property ◽

Multi Class Classification ◽

Level Fusion

Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.

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