19-03-2014, 11:13 AM
Implemented a Novel Multimodal Biometric System using Fingerprint and Iris with Fuzzy Logic.
ABSTRACT
In this paper, they have saved fingerprint feature vectors in the form of 128 bit codes which consisted of 68 bit each of terminations and bifurcations. Instead of saving both the terminations and bifurcations, we are going to save the terminations(X and Y positions) in a 60 bit feature vector. The reason for not saving the bifurcation is that, in some of our images, there are no bifurcations at all or very few bifurcation points available. For extracting iris codes, in this paper, they have first obtained a rectangular region of the iris image with iris visible in the center. Then, the center pixel is obtained by dividing the row and column. Certainly the pixel is in the pupil region and its clear pupil is the darker part in eye, so it can move right to the pixel with a high amount of difference intensity and mark it, move left to the pixel with a high amount of difference intensity and mark it and find the center of these points.Same method is applied to find top and bottom and center of them. Now with these center and peripheral acquired points we can find the real pupil center with center point and maximum distance drawing a pupil circle performing the same task to find the iris region and extract iris from eye image. With Gabor filter features and iris code is extracted. We are going to use canny edge detection method for segmentation and Daugmain’s rubber sheet model for normalization. A fuzzy logic method is used for fusion which is given better performance and accuracy.Fuzzy logic is a kind of soft computing, which mimics human decision making. In our implementation, we are going to use a single hamming distance matcher to compute the final result.
ABSTRACT
In this paper, they have saved fingerprint feature vectors in the form of 128 bit codes which consisted of 68 bit each of terminations and bifurcations. Instead of saving both the terminations and bifurcations, we are going to save the terminations(X and Y positions) in a 60 bit feature vector. The reason for not saving the bifurcation is that, in some of our images, there are no bifurcations at all or very few bifurcation points available. For extracting iris codes, in this paper, they have first obtained a rectangular region of the iris image with iris visible in the center. Then, the center pixel is obtained by dividing the row and column. Certainly the pixel is in the pupil region and its clear pupil is the darker part in eye, so it can move right to the pixel with a high amount of difference intensity and mark it, move left to the pixel with a high amount of difference intensity and mark it and find the center of these points.Same method is applied to find top and bottom and center of them. Now with these center and peripheral acquired points we can find the real pupil center with center point and maximum distance drawing a pupil circle performing the same task to find the iris region and extract iris from eye image. With Gabor filter features and iris code is extracted. We are going to use canny edge detection method for segmentation and Daugmain’s rubber sheet model for normalization. A fuzzy logic method is used for fusion which is given better performance and accuracy.Fuzzy logic is a kind of soft computing, which mimics human decision making. In our implementation, we are going to use a single hamming distance matcher to compute the final result.