Template Matching Analysis using Neural Network for Mobile Iris Recognition System

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

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/jcini.2008070104 ◽

2008 ◽

Vol 2 (3) ◽

pp. 43-56 ◽

Cited By ~ 10

Author(s):

Guangzhu Xu ◽

Zaifeng Zang

Keyword(s):

Neural Network ◽

Iris Recognition ◽

Recognition System ◽

Cortical Model

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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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Architecture Design for Feature Extraction and Template Matching in a Real-Time Iris Recognition System

Electronics ◽

10.3390/electronics10030241 ◽

2021 ◽

Vol 10 (3) ◽

pp. 241

Author(s):

Hau Ngo ◽

Ryan Rakvic ◽

Randy Broussard ◽

Robert Ives ◽

Matthew Carothers

Keyword(s):

Feature Extraction ◽

Real Time ◽

Approximation Method ◽

Template Matching ◽

High Performance ◽

Iris Recognition ◽

Recognition System ◽

Architecture Design ◽

Pipeline Architecture ◽

On Chip

Real-time support for an iris recognition algorithm is a considerable challenge for a portable system that is commonly used in the field. In this paper, an efficient parallel and pipeline architecture design for the feature extraction and template matching processes in the Ridge Energy Direction (RED) algorithm for iris recognition is presented. Several techniques used in the proposed architecture design to reduce the computational complexity while supporting a high performance capability include (i) a circle approximation method for the iris unwrapping process, (ii) a parallel design with an on-chip buffer for 2D convolution in the feature extraction process, and (iii) an approximation method for log2 and inverse-log2 conversion in the template matching process. Performance analysis shows that the proposed architecture achieves a speedup of 881 times compared to the conventional method. The proposed design can be integrated with an embedded microprocessor to realize a complete system-on-chip solution for a portable iris recognition system.

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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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