| Efficient Optical Deep Learning Model Based on Cycle-GAN for Secure Face Recognition |
| Paper ID : 1101-ICEEM2025 (R1) |
| Authors |
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Ensherah Naeem *1, Atef Abouelazm2, Walid El-Shafai3, El-Sayed M. El-Rabaie4, Abeer S. Salman5 1Ensherah A. Naeem, Department of Electrical, Faculty of Technology and Education, Suez University, P. O. Box:43221, Suez, Egypt 2Atef Abouelazm Department of Electronics and Electrical Communications Faculty of Electronic Engineering,Manoufia University Menouf, Egypt 3Walid El-Shafai Department of Electronics and Electrical Communications Faculty of Electronic Engineering,Manoufia University Menouf, Egypt 4El-Sayed M. El-Rabaie Department of Electronics and Electrical Communications Faculty of Electronic Engineering,Manoufia University Menouf, Egypt 5Abeer S. Salman Department of Electronics and Electrical Communications Faculty of Electronic Engineering,Manoufia University Menouf, Egypt |
| Abstract |
| In the past few decades, biometric authentication has been an increasingly popular practice. In place of the conventional passwords, biometrics traits are used to secure access to computers. However, if these traits are used in their original form, they can only be used once. Therefore, it is perfect to use a biometric template that can be altered if it is attacked. This can be achieved through cancelable biometrics. Enhancing the security and privacy of biometric authentication is the goal of cancelable biometrics. It is effective to create cancelable biometric templates using deep learning. To ensure safe biometric information in confirmation schemes, an efficient encryption algorithm is developed in this research. The face is the biometric taken into consideration in this paper. We use the Cycle-Generative Adversarial Networks (Cycle GANs) which is one of the deep learning techniques, and a confusion baker map. The Cycle GAN has a generator and a discriminator. The generator alters the original image to create an unreal one, and the discriminator tries to determine whether it came from the generator or not. A set of biometric faces were used for training of this model. The generated templates have been used to test the cancelable biometric system. Moreover, the noise effect has been taken into consideration. Several metrics have been considered to test the system including Equal Error Rate (EER), False Accept Rate (FAR), False Reject Rate (FRR), and Area under the Receiver Operator Characteristics Curve (AROC). |
| Keywords |
| Cancelable biometrics; Deep learning; Cycle GAN; EER; AROC |
| Status: Accepted |