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Secure cancelable face recognition system based on inverse filter

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Abstract

Biometric systems have recently become popular for modern security applications. Unfortunately, these systems have been the target of many hacking attempts. Biometrics in biometric databases will be lost forever if they are hacked and stolen. As a result, there is an urgent need to implement modern upgradable biometric systems. Cancelable biometrics is based on the principle of transforming biometric data to alternate templates that cannot be easily exploited by an impostor or attacker and can be discarded if violated. The idea in this paper depends on the inverse filter in a cancelable face recognition system. Masked biometric images are produced in the proposed cancelable face recognition system by blurring, noise addition, and then inverse filtering. In image restoration theory, it is well understood that inverse filtering contributes to noise enhancement, which is an undesirable effect. On the contrary, this result would be desired in cancelable biometric systems. When noise is magnified, it can obscure the original biometrics, resulting in cancelable templates. This is the philosophy that underpins the proposed system. The proposed system was tested on the Olivetti and Oracle (ORL) dataset, the Labeled Faces in the Wild (LFW) database, and the Face Recognition Technology (FERET) database. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, false positive rate, false negative rate, Equal Error Rate (EER), decidability, correlation coefficient, and Area under the Receiver Operating Characteristic curve (AROC) show that the proposed cancelable biometric recognition system is quite effective for several security applications.

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Acknowledgement

The researchers would like to acknowledge Deanship of Scientific Research, Taif University for funding this work.

Funding

The researchers would like to acknowledge Deanship of Scientific Research, Taif University for funding this work.

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Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Abd El-Rahman Farouk, Walid El-Shafai, Adel S. El-Fishawy, Moawad I. Dessouky, El-Sayed M. El-Rabaie & Fathi E. Abd El-Samie

  2. Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Mohammed Abd-Elnaby

  3. Department of Electronics and Electrical Communications, Bilbis Higher Institute of Engineering, Bilbis, Sharqia, Egypt

    Huda I. Ashiba

  4. Department of Automatic Control, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El-Banby

  5. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  6. Department of Information Technology College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

Authors
  1. Abd El-Rahman Farouk
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  2. Mohammed Abd-Elnaby
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  3. Huda I. Ashiba
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  4. Ghada M. El-Banby
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  5. Walid El-Shafai
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  6. Adel S. El-Fishawy
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  7. Moawad I. Dessouky
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  8. El-Sayed M. El-Rabaie
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  9. Fathi E. Abd El-Samie
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Correspondence to Walid El-Shafai.

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Farouk, A.ER., Abd-Elnaby, M., Ashiba, H.I. et al. Secure cancelable face recognition system based on inverse filter. J Opt (2024). https://doi.org/10.1007/s12596-023-01233-7

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