Image Quality Assessment for Fake Biometric Detection
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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.
- Edge detection
- Feature vector
DRAW BACKS OF EXISTING SYSTEMS
- 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.
- We pre-process the eye image by applying downscaling and colour level transform. The downscaling of the eye image is done to reduce the search area for pupil and iris boundaries
- Biometric system based on the combination of iris palm print and finger print features for person authentication using Sequential modified haar wavelet and Energy feature extraction using key generation analysis.
- 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
- Pattern Recognition
- MATLAB 7.5 and above versions
DETECTING AUTHENTICATED AND UN-AUTHENTICATED PERSON