Image Quality Assesment Using Fake Biometric Detection
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.
- Edge detection
- Feature vector
- 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.
- 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.
- 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
- 4 GB of RAM
- 500 GB of Hard disk
- MATLAB 2018b
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