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Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification.
Yinyin Wei1,2, Xiangyang Zhang3, Aijun Zeng1
1Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China.
Sensors (Basel, Switzerland)
|October 27, 2022
Summary
This study introduces an efficient iris recognition system that accurately localizes and verifies irises, even with noisy images from real-world conditions. The new lightweight deep learning model achieves high accuracy with fewer parameters, enhancing security applications.
Area of Science:
- Computer Science
- Biometrics
- Artificial Intelligence
Background:
- Biometric recognition, particularly iris recognition, is crucial for security but struggles with noisy, unnormalized images from non-cooperative environments.
- Current deep learning methods for iris recognition often increase model complexity and neglect the essential iris localization stage.
Purpose of the Study:
- To develop an effective and efficient iris recognition scheme addressing challenges in non-cooperative environments.
- To improve iris localization accuracy and speed.
- To create a lightweight deep learning model for high-precision iris verification with reduced complexity.
Main Methods:
- Iris localization using parallel Hough circle transform and the Daugman algorithm.
- Iris verification employing a novel lightweight convolutional neural network with deep residual network modules and residual pooling.
- Experiments conducted on multiple iris datasets collected under non-cooperative conditions.
Main Results:
- Significantly accelerated iris localization speed (21-36x) on GPU compared to CPU across four datasets.
- Achieved effective iris localization accuracy.
- Demonstrated high-precision iris verification with a lightweight network, yielding low equal error rates (1.01%-1.71%) across four databases.
Conclusions:
- The proposed scheme effectively addresses iris recognition challenges in non-cooperative environments.
- The novel lightweight CNN offers a balance of high accuracy and reduced model complexity for iris verification.
- This approach enhances the practicality and efficiency of iris recognition systems for security applications.
