Saif Mohanad Kadhim, Johnny Siaw Paw, yaw tak, Shahad Ameen and Ahmed Alkhayyat
Adv. Artif. Intell. Mach. Learn., 5 (1):3446-3464
1. Saif Mohanad Kadhim: College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia
2. Johnny Siaw Paw: Institute of Sustainable Energy, Universiti Tenaga Nasional (National Energy University), Selangor, Malaysia
3. yaw tak: Institute of Sustainable Energy, Universiti Tenaga Nasional (National Energy University), Selangor, Malaysia
4. Shahad Ameen: Institute of Sustainable Energy, Universiti Tenaga Nasional (National Energy University), Selangor, Malaysia
5. Ahmed Alkhayyat: Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq
DOI: 10.54364/AAIML.2025.51197
Article History: Received on: 13-Jan-25, Accepted on: 18-Mar-25, Published on: 25-Mar-25
Corresponding Author: Saif Mohanad Kadhim
Email: PE21093@student.uniten.edu.my
Citation: Saif Kadhim, et al. Deep Learning for Robust Iris Recognition: Introducing Synchronized Spatiotemporal Linear Discriminant Model-Iris. Advances in Artificial Intelligence and Machine Learning. 2025;5(1):197.
A novel Synchronized Spatiotemporal Linear
Discriminant Model –Iris (SSLDMNet-Iris,) a deep learning architecture is
introduced in this work which is designed to address the challenges associated
with iris recognition under varying environments, such as occlusion, variations
in eye pupil dilation, and lower image quality. This has been implemented by
integrating multi-scale convolutional feature extraction with synchronized
temporal modeling through Gated Recurrent Units (GRUs), the proposed
SSLDMNet-Iris model effectively can catch both intricate texture details and
global spatial patterns related to the iris. Additionally, the model utilizes
Fisher's Linear Discriminant (FLD) for features extraction and optimizing the separation between classes while minimizing
intra-class variance, thereby raising recognition
accuracy. Comprehensive experiments conducted on seven benchmark
datasets (i.e., CASIA Iris 1.0, CASIA Iris 2.0, CASIA Iris 3.0, CASIA Iris 4.0,
IITD, UBIRIS, MMU), and exhibit a promising
accuracy rate where, the SSLDMNet-Iris surpassing traditional models like
VGG16, AlexNet, and ResNet. Notably, SSLDMNet-Iris attains 100% accuracy on
CASIA Iris 1.0, CASIA Iris 2.0, and MMU datasets, while maintaining high
computational efficiency with a reduced processing time. These results
highlight the robustness and versatility of SSLDMNet-Iris, making it an ideal
candidate for real-time iris recognition applications.