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

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Abstract

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.

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Acknowledgements

The authors thank the IMPRINT (IMP/2018/000098) project for providing the computational facilities in the speech and image processing laboratory in NIT Silchar. The authors are also thankful to Mohd Iqbal, Mewat Engineering College, Nuh, Haryana, for the moral support.

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Authors and Affiliations

  1. Electronics and Communication Engineering, National Institute of Technology, Silchar, India

    Naseem Ahmad, Kuldeep Singh Yadav, Anish Monsley Kirupakaran, Saharul Alom Barlaskar, Rabul Hussain Laskar & Ashraf Hossain

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  1. Naseem Ahmad
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  2. Kuldeep Singh Yadav
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Ahmad, N., Yadav, K.S., Kirupakaran, A.M. et al. Design and development of an integrated approach towards detection and tracking of iris using deep learning. Multimed Tools Appl 83, 44661–44683 (2024). https://doi.org/10.1007/s11042-023-17433-z

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