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A robust cancellable face and palmprint recognition system based on 3D optical chaos-DNA cryptosystem

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Abstract

Several cancelable biometric procedures have been developed to preserve personal data security. This research presents a cancelable biometric authentication mechanism to ensure personal data security, while maintaining the biometric templates secure from hackers. Therefore, our major approach is to provide a new authentication system based on 3D chaotic maps, Piecewise Linear Chaotic Map (PWLCM), logistic map, and Deoxyribonucleic Acid (DNA) sequencing theory. The approach is based on confusion as well as a diffusion. Firstly, a 3D chaotic map is made up to allow 3D chaos creation, chaos histogram equalization, row rotation, column rotation, and XOR operation. Secondly, the PWLCM and the Logistic Map are used for generating the main values required to make use of the DNA theory to generate the key image. Thirdly, DNA theory is applied as a second encryption algorithm on the output of the 3D chaotic algorithm. Fourthly, the intermediate image is decoded, rotated 90 degrees anticlockwise, and used in the following step in the DNA encoding process. Finally, step 3 is repeated on columns to acquire the final encrypted image. The proposed framework hides all discriminative properties of biometric templates, resulting in a fully unspecified biometric pattern. Various face, as well as palmprint biometric datasets, have been examined and evaluated. Various evaluation factors are employed to examine the efficiency of the recommended cryptography algorithm including Area under the Receiver Operating Characteristic (AROC), False Acceptance Rate (FAR), histogram analysis, Histogram Deviation (\(D_{H}\)), correlation coefficient, Structural Similarity Index Metric (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The computational results prove that the proposed cryptosystem is trustworthy. A comparative study of the recommended approach with various strategies is also provided. Simulation studies illustrate that the suggested approach produces an average AROC of approximately 1, an average FAR of \(6.2 \times 10^{ - 3}\), an average \(D_{H}\) of 0.8755, and finally, an average PSNR of 8.2061.

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Availability of data and materials

All data are available upon request from the corresponding author.

Abbreviations

DNA:

Deoxyribonucleic acid

PWLCM:

Piecewise linear chaotic map

EER:

Equal error rate

AROC:

A receiver operating characteristic curve

FAR:

False acceptance rate

\(D_{H}\) :

Histogram deviation

PINs:

Personal identification numbers

DSP:

Digital signal processing

DRPE:

Double random phase encoding algorithm

RPM 1:

Random phase mask

RPM 2:

Random phase mask

FRFT:

Fractional Fourier transform

AF:

“À Trous” filtering

AT:

À Trous transform algorithm

A:

Adenine

T:

Thymine

C:

Cytosine

G:

Guanine

XOR:

Exclusive OR

MD5:

Message-digest mechanism 5

PSNR:

Peak signal to noise ratio

SSIM:

Structural similarity index metric

TPF:

True positive rate

FPF:

False positive rate

PTD:

Probability of true distribution

PFD:

Probability of false distribution

Var(G):

Variance (genuine correlation)

Var(F):

Variance (fake correlation)

\(x_{n}\) :

The initial value of the chaos map \(0 < x_{n} < 1\)

\(y_{n}\) :

The initial value of chaos map \(0 < y_{n} < 1\)

\(z_{n}\) :

The initial value of chaos map \(0 < z_{n} < 1\)

\(\mu\) :

Constant parameter = 4

\({\upgamma }\) :

Control parameter \(3.53 < \gamma < 3.81\)

\(\beta\) :

Control parameter \({ }0 < \beta < 0.022{ }\)

\(\alpha\) :

Control parameter \(0 < \alpha < 0.015\)

\(N_{2} ,N_{4} , N_{6}\) :

Random numbers > 10,000

\(M\) :

Number of columns

\({\text{N}}\) :

Number of rows

\(p\) :

Control parameter \(p \in \left( {0,0.5} \right)\)

\(d_{1} ,d_{2} ,d_{3} ,d_{4}\) :

Each of them represents a single byte

pixel:

The pixel value of the key image

Rule:

One of the 8 rules of DNA

\(process\) :

One of DNA operations \(\left( {XOR, + , - } \right)\)

\(C_{i,j}\) :

The correlation coefficient

\(i,j\) :

The grayscale values of two neighboring pixels in the image

\(E\) :

The expectation

\(E\left( i \right)\) :

Mean of \(i\)

\(E\left( j \right)\) :

Mean of \(j\)

\(\sqrt {D\left( i \right)}\) :

Standard deviation of \(i\)

\(\sqrt {D\left( j \right)}\) :

Standard deviation of \(j\)

\({\upmu }_{{\text{I}}}\) :

The average of an input image (I)

\(\mu_{T}\) :

The average of the encrypted image

\(\sigma_{I}^{2}\) :

The variance of the input image

\(\sigma_{T}^{2}\) :

The variance of the encrypted image

\(\sigma_{IT}\) :

Signifies the covariance between the input and encrypted images

\(S_{1}\) and \(S_{2}\) :

Small values

\(q_{i}\) :

The absolute difference between the pure and ciphered images' estimated histograms at a gray level i

n :

The number of bits/pixel

\(O\) :

The reference image

\(T\) :

The encrypted image

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Acknowledgements

The authors are very grateful to all the institutions in the affiliation list for successfully performing this research work. The authors would like to thank Prince Sultan University for their support.

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Authors and Affiliations

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

    Haidy A. Ali Eldawy, Walid El-Shafai & Fathi E. Abd El-Samie

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

    Walid El-Shafai

  3. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ezz El-Din Hemdan

  4. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El-Banby

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  1. Haidy A. Ali Eldawy
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  4. Ghada M. El-Banby
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Eldawy, H.A.A., El-Shafai, W., Hemdan, E.ED. et al. A robust cancellable face and palmprint recognition system based on 3D optical chaos-DNA cryptosystem. Opt Quant Electron 55, 970 (2023). https://doi.org/10.1007/s11082-023-04840-7

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