Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Khalifa, N. E. M., M. H. N. Taha, A. E. Hassanien, and H. N. E. T. Mohamed, "Deep Iris: Deep Learning for Gender Classification Through Iris Patterns", Acta Informatica Medica, vol. 27, issue 2, pp. 96-102, 2019. copy at


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.

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