Image Quality Assessment for Fake Biometric Detection

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Description

ABSTRACT:-   

This paper presents the fusion of three biometric traits, i.e., iris, face, and fingerprint, at matching score level architecture using a weighted sum of score technique. The features are extracted from the pre-processed images of the iris, face, and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., Pre-Processing, DWT Segmentation, and Image fusion. The final fusion is then used to declare the person as Authenticate or Un-Authenticate with Secret Key Analysis.     


System Analysis

   Existing Systems

  • Edge detection
  • Segmentation
  • Feature vector 

DISADVANTAGE

  • Existing is done using Fingerprinting. Fingerprinting is that much not flexible because we can make duplicates of fingers and bluff people. It is not that much efficient.
  • Only the spatial domain is calculated.

Proposed Systems

  • Biometric system based on the combination of iris palm print and fingerprint features for person authentication
  • We will be using PCA i.e. Principal Component Analysis algorithm to find out co-variance and variance. 

Advantages

  • The Sequential Haar coefficient requires only two bytes to store each of the extracted coefficients. 
  • The cancellation of the division in subtraction results avoids the usage of decimal numbers while preserving the difference between two adjacent pixels.
  • This system gives more security compared to a uni-modal system because of two biometric features  

Block Diagram

Image Quality Assessment for Fake Biometric Detection
Image Quality Assessment for Fake Biometric Detection

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

SOFTWARE REQUIREMENTS:

  • MATLAB 2018b

REFERENCES:

[1] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE Security Privacy, vol. 1, no. 2, pp. 33–42, Mar./Apr. 2003.

[2] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,” in Proc. AWB, 2004.

[3] J. Galbally, C. McCool, J. Fierrez, S. Marcel, and J. Ortega-Garcia, “On the vulnerability of face verification systems to hill-climbing attacks,” Pattern Recognit., vol. 43, no. 3, pp. 1027–1038, 2010.

[4] A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” EURASIP J. Adv. Signal Process., vol. 2008, pp. 113–129, Jan. 2008.

[5] J. Galbally, F. Alonso-Fernandez, J. Fierrez, and J. Ortega-Garcia, “A high-performance fingerprint liveness detection method based on quality related features,” Future Generation. Comput. Syst., vol. 28, no. 1, pp. 311–321, 2012.

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