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Open AccessArticle

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
School of Computer Electronics and Information, Guangxi Key Laboratory of Multimedia Communication and Network Technology, Guangxi University, Nanning 530004, China
3
Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
*
Authors to whom correspondence should be addressed.
Electronics 2019, 8(10), 1109; https://doi.org/10.3390/electronics8101109
Received: 30 August 2019 / Revised: 23 September 2019 / Accepted: 23 September 2019 / Published: 1 October 2019
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations. View Full-Text
Keywords: classification; iris; genetically identity; deep learning; convolutional neural network classification; iris; genetically identity; deep learning; convolutional neural network
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Fang, B.; Lu, Y.; Zhou, Z.; Li, Z.; Yan, Y.; Yang, L.; Jiao, G.; Li, G. Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network. Electronics 2019, 8, 1109.

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