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Deep Iris: Deep Learning for Gender Classification Through Iris Patterns.

Nour Eldeen M Khalifa1, Mohamed Hamed N Taha1, Aboul Ella Hassanien1,2

  • 1Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|August 28, 2019
PubMed
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This study introduces a deep convolutional neural network for iris gender identification, achieving 98.88% accuracy. The novel method enhances security surveillance and forensic applications.

Keywords:
Deep Convolutional Neural NetworkDeep LearningDeep NeuralSoft Biometricsgender-identification

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Soft biometrics is a rapidly growing research area in computer science.
  • Iris recognition is a key biometric modality for identification.

Purpose of the Study:

  • To develop a robust method for identifying a person's gender from iris images.
  • To enhance security surveillance and forensic applications through accurate iris-based gender identification.

Main Methods:

  • A deep convolutional neural network (CNN) architecture was designed for iris gender identification.
  • Graph-cut segmentation was employed to isolate the iris region from background noise.
  • The CNN model comprised convolutional layers for feature extraction and fully connected layers for classification.

Main Results:

  • The proposed model achieved a testing accuracy of 98.88% on an augmented dataset.
  • Data augmentation techniques were utilized to increase dataset size to 9,000 images and mitigate overfitting.
  • Performance was validated against existing methods using the same dataset.

Conclusions:

  • The developed deep convolutional neural network model significantly outperformed existing approaches in iris-based gender identification accuracy.
  • The method shows promise for improving the effectiveness of security and forensic systems.