An experimental study of deep convolutional features for iris recognition

Shervin Minaee, Amirali Abdolrashidiy, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509067138
DOIs
StatePublished - Feb 7 2017
Event2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Philadelphia, United States
Duration: Dec 3 2016 → …

Publication series

Name2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings

Other

Other2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016
CountryUnited States
CityPhiladelphia
Period12/3/16 → …

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering

Cite this

Minaee, S., Abdolrashidiy, A., & Wang, Y. (2017). An experimental study of deep convolutional features for iris recognition. In 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings [7846859] (2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2016.7846859