Oral Presentation , Page 22-28 (7)

Volume Title: 2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021

¹Abd elrahman Farouk Al libady ; ²huda ashiba; ³ghada banby; ⁴Adel shaker el fishawy; ⁵Moawad .Dessouky; ⁶Fathi Abd El-SAmie; ⁴sayed rabaie

¹1Department of Electronics and Electrical Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf,Egypt

²Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Zagazige University, Egypt

³Department of Industrial electronics and control engineering Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt

⁴Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University, Egypt

⁵Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menoufia University, Egypt

⁶Minufia- Egypt

More recently, biometric systems have spread for modern security applications. Unfortunately, these systems have experienced several attempts of hacking. If biometric databases are compromised and stolen, biometrics in these databases will be lost forever. Consequently, there is an immediate need to introduce new upgradable biometric systems. The concept behind cancelable biometrics is to convert biometric data to alternative templates, which cannot be easily used by the impostor or intruder, and can be eliminated if breached. In this paper, the inverse filter is utilized in a cancelable face recognition system. In this system, masked biometric images are generated by blurring, noise addition and then inverse filtering. It is well-known in the image processing theory that inverse filtering leads to noise enhancement, which is an undesired effect in image restoration. In contrary, this effect will be desired in cancelable biometric systems. If the noise is magnified with an appropriate extent, it can mask the original biometrics leading to cancelable templates. This is the theory behind the proposed system. The proposed system is applied on the Olivetti and Oracle (ORL) dataset. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Decidability, correlation coefficient, Area under the Receiver Operating Characteristic (AROC) curve demonstrate that the proposed system is resistant to intruders and hackers. Hence, it is efficient for several security applications.

Face images; Biometrics; Cancelable biometric recognition; Inverse filtering; Non-invertibility; Unlinkability