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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