Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network
Beihua Fang, Yuanfu Lu, Zhisheng Zhou, Zhihui Li, Yuwen Yan, Linfeng Yang, Guohua Jiao, Guangyuan Li · Electronics · 2019
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.