Home|Journals Follow on Twitter| Subscribe to List

Directory for Medical Articles
 

Open Access

-



Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Nour Eldeen M. Khalifa; Mohamed Hamed N. Taha; Aboul Ella Hassanien; Hamed Nasr Eldin T. Mohamed.

Abstract
Introduction: One attractive research area in the computer science field is soft biometrics. Aim: To Identify a person’s gender from an iris image when such identification is related to security surveillance systems and forensics applications. Methods: In this paper, a robust iris gender-identification method based on a deep convolutional neural network is introduced. The proposed architecture segments the iris from a background image using the graph-cut segmentation technique. The proposed model contains 16 subsequent layers; three are convolutional layers for feature extraction with different convolution window sizes, followed by three fully connected layers for classification. Results: The original dataset consists of 3,000 images, 1,500 images for men and 1,500 images for women. The augmentation techniques adopted in this research overcome the overfitting problem and make the proposed architecture more robust and immune from simply memorizing the training data. In addition, the augmentation process not only increased the number of dataset images to 9,000 images for the training phase, 3,000 images for the testing phase and 3,000 images for the verification phase but also led to a significant improvement in testing accuracy, where the proposed architecture achieved 98.88%. A comparison is presented in which the testing accuracy of the proposed approach was compared with the testing accuracy of other related works using the same dataset. Conclusion: The proposed architecture outperformed the other related works in terms of testing accuracy.

Key words: gender-identification;Deep Learning;Deep Convolutional Neural Network;Soft Biometrics;Deep Neural



Share this Article


Advertisement
Journal of Behavioral Health

SUBMIT YOUR ARTICLE NOW


ScopeMed.com
BiblioMed Home
Follow ScopeMed on Twitter
Author Tools
eJPort Journal Hosting
About BiblioMed
License Information
Terms & Conditions
Privacy Policy
Contact Us

The articles in Bibliomed are open access articles licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
ScopeMed is a Database Service for Scientific Publications. Copyright ScopeMed Information Services.