Design and development of an integrated approach towards detection and tracking of iris using deep learning

Iris detection and tracking play an essential role in a wide range of real-world applications, including tracking gaze, biometric authentication, virtual mouse, smart wheelchair, robotic ophthalmic surgery, etc. However, iris detection is difficult due to specular reflection, occlusion, glistening o...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-05, Vol.83 (15), p.44661-44683
Main Authors: Ahmad, Naseem, Yadav, Kuldeep Singh, Kirupakaran, Anish Monsley, Barlaskar, Saharul Alom, Laskar, Rabul Hussain, Hossain, Ashraf
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Eye (anatomy)
Integrated approach
Localization
Multimedia Information Systems
Occlusion
Robotic surgery
Special Purpose and Application-Based Systems
Specular reflection
Tracking
Wheelchairs
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Summary:Iris detection and tracking play an essential role in a wide range of real-world applications, including tracking gaze, biometric authentication, virtual mouse, smart wheelchair, robotic ophthalmic surgery, etc. However, iris detection is difficult due to specular reflection, occlusion, glistening on glasses, the distance between a person's eye and the camera, etc. Therefore, an integrated approach for iris detection and tracking in an uncontrolled environment is proposed. The proposed approach consists of (i) Tiny-YOLOv3-based eye detection, (ii) Seg-Net for iris detection/segmentation, and (iii) a KLT algorithm for iris tracking. Tracking reduces the computational complexity after the segmentation/localization of the iris in the initial frame instead of detection/segmentation across each frame. The models are evaluated on various benchmark databases (i) BioID, (ii) GI4E, (iii) Talking-Face, and (iv) NITSGoP databases for eye detection, GI4E and NITSGoP databases for iris segmentation, and localization. The complete (eye detection, iris segmentation, iris tracking) model is evaluated on NITSGoP and UPNA head poses databases for iris tracking. Extensive experiments show that our proposed method outperforms the baseline methods. The results also indicate that the proposed method can overcome the iris segmentation in each frame to reduce the computational complexity.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-17433-z