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

Image Quality Assessment

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

6. Block Diagram:

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

8. Application: Pattern Recognition Authentication

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.