If you find this notebook useful in your research or projects, please consider citing it. Proper citation helps me gain recognition for my work and allows others to follow and build upon it.
Sondos Aabed, An End-to-end segmentation-free approach Iris Biometric Authentication, Open Source Contribution (GitHub & Kaggle), May 2024. Available at: Github and Kaggle and ResearchGate
Aabed, Sondos. (2024). An End-to-end segmentation-free approach for Iris eye Recognition. 10.13140/RG.2.2.36208.06407
In this project, a Biometric Authentication system using the Iris biometric authentication method is designed. The approach taken is using the CASIA-Thousand-IRIS dataset and model it using the Deep Convultional Neural Network Architicture, with the minimum image-preprocessing such as resizing with keeping the aspect ratiio and normalization. It is an end-to-end technique without performing segmentaion of the IRIS itself. The results are promising, even without perfroing training on augmentation, the testing accuracy has reached (91.10%). Finally, for the proof of the (biometric authentication system concept) a simple mobile application is designed and the model is deployed on it (IrisRecognizer) as it was exported to it's liter version were default quantization is performed.
In a biometric authentication system there has to be an authenticter part for it. A simple mobile application is done for the concept:
Refer to this repository: https://github.com/sondosaabed/IrisRecognizer