TY - GEN
T1 - An experimental study of deep convolutional features for iris recognition
AU - Minaee, Shervin
AU - Abdolrashidiy, Amirali
AU - Wang, Yao
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/7
Y1 - 2017/2/7
N2 - Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.
AB - Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.
UR - http://www.scopus.com/inward/record.url?scp=85016053252&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016053252&partnerID=8YFLogxK
U2 - 10.1109/SPMB.2016.7846859
DO - 10.1109/SPMB.2016.7846859
M3 - Conference contribution
AN - SCOPUS:85016053252
T3 - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
BT - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016
Y2 - 3 December 2016
ER -