Transfer Learning with Convolutional Neural Networks for IRIS Recognition

Alaslni, Maram.G; Elrefaei, Lamiaa A.

doi:10.5121/ijaia.2019.10505

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…This is referred to as transfer learning. Transfer learning is a deep learning technique that employs existing networks as a starting point for learning new tasks [9]. Transfer learning involves removing a specific task layer from an existing network and adding a new layer so that it can be trained to learn new features for some of the new tasks.…”

Section: Resultsmentioning

confidence: 99%

“…The second part is a classification that uses layers fully connected. The layer description is shown in table 1 for further details [9]. The convolution process was performed on the input image using a set of filters called the kernel.…”

Section: Cnn Architecturementioning

confidence: 99%

“…It's the main step of many object recognition and computer vision tasks to extract effective features. Several researchers therefore focused on robust features for a range of tasks of image classification [9]. Currently, much attention is paid to learning algorithms and revolutionary networks (CCN).…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network for object Identification and Detection

Andini

Rouza

Fimawahib

et al. 2022

J. Phys.: Conf. Ser.

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The goal of this study is to use a Convolutional Neural Network to find the optimum architectural model for classifying cloud images. Cirrus Cumulus Stratus Nimbus uses a source dataset that includes 11 cloud classifications and 2545 cloud photos (CCSN). In this study, the best Convolutional Neural Network is retrained almost fast by transferring education from Google’s basic design. Based on the modified Googlenet architecture, the training and testing phases of the classification process are divided into two. The dataset is separated into three sections during the training phase: 70% of the training data, 15% of the validation data, and 15% of the test data. There are two trials to categorize cloud photographs during the test phase, one of which has ten cloud kinds that can be randomly chosen. The precision achieved throughout the training was 44.5%, according to the findings. The results of the two tests are 75%, with an average error of 0.2. In the testing phase, the percentage is 75%.

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Section: Resultsmentioning

confidence: 99%

Section: Cnn Architecturementioning

confidence: 99%

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Convolutional Neural Network for object Identification and Detection

Andini

Rouza

Fimawahib

et al. 2022

J. Phys.: Conf. Ser.

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“…On the other hand, reducing the size of the convolutional kernels corresponds to performing more non-linear mapping, which helps to enhance the model fitting ability. [63] uses the segmented iris images by Hough transform and Canny edge detection to train the VGG-16. Tests were performed on the IITD, CASIA-Iris-V1, CASIA-Iris-Thousand, and CASIA-Iris-Interval, obtaining accuracies of 100%, 98.3%, 95%, and 91.6%, respectively.…”

Section: Transfer Learning-based Methodsmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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“…Compression methods have been shown to reduce the size of VGG16 by up to 35 times with minimal loss in accuracy. The VGG16 model had the highest recognition accuracy, according to a study that assessed the effectiveness of three deep learning models for iris recognition, including AlexNet, VGG16, and VGG19 [21]. In this study, the VGG16 model was used due to the dataset obtained is in small quantity, so it is sufficient to use VGG16.…”

Section: Cnn Modelmentioning

confidence: 99%

Online Multimodal Compression using Pruning and Knowledge Distillation for Iris Recognition

Samihah Abdul Latif,

Khairul Azami Sidek,

Eddyzulham Abu Bakar

et al. 2024

ARASET

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Deep learning models have advanced to the forefront of image recognition tasks, resulting in high-performing but enormous neural networks with millions to billions of parameters. Yet, deploying these models in production systems imposes considerable memory limits. Hence, the research community is increasingly aware of the need for compression strategies that can reduce the number of model parameters and their resource requirement. Current compression techniques for deep learning models have limitations in efficiency and effectiveness, indicating that more research is required to develop more efficient and practical techniques capable of balancing the trade-offs between compression rate, computational cost, and accuracy. This study proposed a multimodal method by combining multimodal Pruning and Knowledge Distillation techniques for compressing the iris recognition model, which is the size constraint for many image recognition models. To maintain accuracy while shrinking the model’s size, the models are trained, compressed, and further retrained in the downstream job. The analysis includes both fully connected and convolutional layers. Experimentally, the findings show that the proposed technique can achieve 91% accuracy, the same as the existing or original model. Besides that, the model compression can reduce the size of the model almost six times, from 529MB to 90MB, which is a significantly reduced rate. The primary outcome of this study is developing a CNN lightweight model for iris recognition technology that can be used on mobile devices and is resource constrained.

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Transfer Learning with Convolutional Neural Networks for IRIS Recognition

Cited by 14 publications

References 33 publications

Convolutional Neural Network for object Identification and Detection

Convolutional Neural Network for object Identification and Detection

Deep Learning for Iris Recognition: A Review

Online Multimodal Compression using Pruning and Knowledge Distillation for Iris Recognition

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