Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

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.

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

“…The 90% percent was divided into 80% for training and 20% for the validation. The selection of 80% for the training and 20% in the validation proved it is efficient in many types of research such as [53][54][55][56][57]. The training data then divided into mini-batches, each of size = 64, such that ( 9 ; 9 ) ∈ (X /0123 ; Y /0123 ); = 1,2, … , > ?…”

“…This research relied on the deep transfer learning CNN architectures to transfer the learning weights to reduce the training time, mathematical calculations and the consumption of the available hardware resources. There are several types of research in [53,58,59] tried to build their architecture, but those architecture are problem-specific and cannot fit the data presented in this paper. The used deep transfer learning CNN models investigated in this research are Alexnet [29], Resnet18 [39], Googlenet [60], The mentioned CNN models had a few numbers of layers if it is compared to large CNN models such as Xception [40], Densenet [42], and Inceptionresnet [61] which consist of 71, 201 and 164 layers accordingly.…”

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset

“…The authors of this research tried first to build their deep neural networks based on the works presented [ 37 – 39 ], but the testing accuracy was not acceptable. So, the alternative way is to use deep transfer learning models.…”

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

“…The proposed models used in this research relied on the deep transfer learning CNN architectures to transfer the learning weights to reduce the training time, mathematical calculations and the consumption of the available hardware resources. There are a number of studies in (31)(32)(33) that have attempted to build their own architecture, but those architectures are problem specific and do not fit the data presented in this paper. The deep transfer learning CNN models investigated in this research are AlexNet (18), ResNet18 (26), SqueezeNet (34)is typically possible to identify multiple DNN architectures that achieve that accuracy level.…”