Identification Based on Iris Detection Technique

Identification Based on Iris Detection Technique

Identification, Biological data measures, Deep Structured-Learning, UniNet, Fully Connected Neural Network, Convolutional Deep Neural Network, Eyeiris detection

Iris-biometrics are an alternative way of authenticating and identifying a person because biometric identifiers are unique to people. This paper introduces a method aims to efficient human identification by enhanced iris detection method within acceptable time. After preparing various type of images, then perform a series of pre-processing steps and standardize them, after that use Uni-Net learning, so identify the human by Navie-Bays method is the last step based on the output of Uni-Net which is role as feature extractor for the iris part and another sub-net for non-iris part that may involve identification-outcome. The outcome of this method looked good compared to some high-level methods, so, was accuracy-rate 9855, 99.25, and 99.81 for CASIA-v4, ITT-Delhi, and MMU-database respectively. Also, this paper introduces a method of iris recognition using CNN model which is improved the preprocessed patterns that together from dataset applied some procedures to develop them based on techniques of equalization and acclimate contrast ones. After that characteristic extracted and classified using CNN that comprises of 10 layers with back-propagation schema and adjusted moment evaluation Adam-optimizer for modernize weights. The overall accuracy was 95.31% with utilization time 17.58 (mints) for training-model.

Shaimaa Hameed Shaker

Farah Qais Al-Kalidi

Raheem Ogla

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 24 (2022); pp. 154-169

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-12-20

Copyright (c) 2022 Shaimaa Hameed Shaker, Farah Qais Al-Kalidi, Raheem Ogla

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article