Personal Authentication System Based Iris Recognition with Digital Signature Technology


  • Huda M Therar Northern Technical University
  • Ahmed J Ali Northern Technical University


Multimodal iris recognition, Convolutional Neural Network (CNN), Biometric Signatures, RSA algorithm


Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, Biometrics are now being used in applications for accessing databases and commercial workflow systems. These applications need to implement measures to counter security threats.  Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most difficult problem is how to keep biometric data while maintaining the practical performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. The implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets respectively after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. The time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056 respectively for the NTU iris dataset. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm.




How to Cite

M Therar, H., & J Ali, A. (2023). Personal Authentication System Based Iris Recognition with Digital Signature Technology. Journal of Soft Computing and Data Mining, 4(1), 13–29. Retrieved from