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Image Quality Assessment for
Fake Biometric
Detection: Application to Iris,
Fingerprint, and Face Recognition
Abstract:
 This paper presents fusion of three biometric traits, i.e., iris, face and
fingerprint, at matching score level architecture using weighted sum
of score technique.
 The features are extracted from the pre-processed images of 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.,
normalization, generation of similarity score and fusion of weighted
scores.
 The final score is then used to declare the person as Authenticate or
Un-Authenticate with Secret Key Analysis.
Existing Systems:
 Edge detection
 Segmentation
 Feature vector
Draw Backs:
 Existing is done using Finger printing .Finger printing 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.
 We will be using PCA i.e. Principal Component Analysis
algorithm to find out co-variance and variance.
Proposed System:
Biometric system based on the combination
of iris palm print and finger print features for
person authentication.
Methodologies:
 Image Selection
 Pre-Processing
 Image Fusion
 Database Loading
 Recognition Process
Block Diagram:
Advantages:
 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 uni-modal
system because of two biometric features
Application:
 Pattern Recognition
 Authentication
Software Requirement:
 MATLAB 7.5 and above versions
(MATLAB is a high-performance language for technical
computing. It integrates computation, visualization, and
programming in an easy-to-use environment where problems
and solutions are expressed in familiar mathematical notation.)
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] K. A. Nixon, V. Aimale, and R. K. Rowe, “Spoof detection
schemes,” Handbook of Biometrics. New York, NY, USA: Springer-
Verlag, 2008, pp. 403–423.

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0TH Image Quality Assessment for Fake BiometricDetection Application to Iris Fingerprint and Face Recognition.pptx

  • 1. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition
  • 2. Abstract:  This paper presents fusion of three biometric traits, i.e., iris, face and fingerprint, at matching score level architecture using weighted sum of score technique.  The features are extracted from the pre-processed images of 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., normalization, generation of similarity score and fusion of weighted scores.  The final score is then used to declare the person as Authenticate or Un-Authenticate with Secret Key Analysis.
  • 3. Existing Systems:  Edge detection  Segmentation  Feature vector
  • 4. Draw Backs:  Existing is done using Finger printing .Finger printing 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.  We will be using PCA i.e. Principal Component Analysis algorithm to find out co-variance and variance.
  • 5. Proposed System: Biometric system based on the combination of iris palm print and finger print features for person authentication. Methodologies:  Image Selection  Pre-Processing  Image Fusion  Database Loading  Recognition Process
  • 7. Advantages:  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 uni-modal system because of two biometric features
  • 9. Software Requirement:  MATLAB 7.5 and above versions (MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.)
  • 10. 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] K. A. Nixon, V. Aimale, and R. K. Rowe, “Spoof detection schemes,” Handbook of Biometrics. New York, NY, USA: Springer- Verlag, 2008, pp. 403–423.