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Dynamic differential annealing-based anti-spoofing model for fingerprint detection using CNN

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Abstract

Data security and privacy play a significant role in human life over the past few years. In the present digital era, advanced technologies utilize wide reliance and ubiquity to assist the counter theft system. Due to the enhanced crime rate, determining the solution becomes a burdensome process to recognize the fingerprint. To overcome such shortcomings, this paper proposes a convolution neural network and dynamic differential annealing (CNN-DDA)-based spoofed fingerprint detection. Here a CNN-DDA approach is proposed to analyze and evaluate the false or forged fingerprint concerning spoof forgery authentication system. The main intention of CNN-DDA architecture employs in investigating a complicated and problematic relationship among various features thus enabling highly detailed features. The proposed CNN-DDA-based spoofed fingerprint detection uses various datasets namely LivDet 2015 and LivDet 2013 for evaluation. Also, the real image set is captured using various fingerprint scanners such as Gelatine, wood glue, ecoflex and modasil. The experimental analysis is conducted for various evaluation measures such as accuracy rate, classification error value rate and processing time. The results revealed that the proposed approach provides high spoofed fingerprint detection with a better accuracy rate, less processing time and classification error.

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Abbreviations

\(M_{{{\text{KJ}}}}^{P}\) :

Weight function between the \(J\,{\text{th}}\) output map and the \(K\,{\text{th}}\) input map

\(Z_{J}^{P}\) :

\(J\,{\text{th}}\) Output feature map present in the \(P\,{\text{th}}\) layer

\(Z_{K}^{P - 1}\) :

\(K\,{\text{th}}\) Output feature map present in the \((P - 1){\text{th}}\) layer

\(*\) :

Bias value

\(F\) :

Activation function

\(D_{P}^{J}\) :

Convolution operation

\(d_{w}\) :

Maximum pool sub-sampling function

\(\gamma_{J}^{P}\) :

Bias function of pooling layer

\(Z_{{}}^{P - 1}\) :

The input vector

\(Z_{{}}^{P}\) :

Final output vectors

\(D_{{}}^{J}\) :

Bias function of fully connected layer

\(w^{p}\) :

Weight among the \(P\,{\text{th}}\) layer and the \((P - 1){\text{th}}\) layer

\(N_{S}^{J}\) :

New solution with respect to J iteration number

\(R_{{{\text{GS}}}}^{{}}\) :

Randomly generated solution

\(R_{{{\text{CS}}}}^{K} \,{\text{and}}\,\,R_{{{\text{CS}}}}^{I}\) :

Randomly chosen solution in terms of K and I index

\(\Delta t\) :

Temperature change

\(F_{G}\) :

Forging parameter

\(R\;{\text{and}}\;{\text{Re}}\) :

Random and the remainder value

\({\text{Pr}}\) :

Probability rate

\(S_{P}\) :

Sub population

\(\Delta D\) :

Fitness value differences

p :

Size of the population

t :

Temperature variable

\(S_{F}\) :

Scaling factor

\(A_{R}\) :

Accuracy

\(n({\text{TF}})\,\,{\text{and}}\,\,n({\text{FF}})\) :

Number of the corrected and false fingerprint

\(n(x)\) :

Total number of fingerprint images recognized as true

\(n(y)\) :

Total number of fingerprint images recognized as false

\(A_{{{\text{TF}}}}\) :

True fingerprint accuracy

\(A_{{{\text{FF}}}}\) :

False fingerprint accuracy

\(E_{{{\text{HTR}}}}\) :

Half total error

\(F_{{{\text{AR}}}}\) :

False acceptance rate

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Authors and Affiliations

  1. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, , Chennai, India

    B. Uma Maheswari, M. P. Rajakumar & J. Ramya

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  1. B. Uma Maheswari
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Correspondence to B. Uma Maheswari.

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Maheswari, B.U., Rajakumar, M.P. & Ramya, J. Dynamic differential annealing-based anti-spoofing model for fingerprint detection using CNN. Neural Comput & Applic 34, 8617–8633 (2022). https://doi.org/10.1007/s00521-021-06758-1

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