S+SSPR 2010 Workshop

444 views

Published on

Feature Level Fusion of Face and Palmprint Biometrics

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
444
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

S+SSPR 2010 Workshop

  1. 1. Dakshina Ranjan Kisku, Phalguni Gupta, Jamuna Kanta Sing Dept. of CSE, Asansol Engineering College, Dept. of CSE, IIT Kanpur, Dept. of CSE, Jadavpur University, India **Contact: drkisku@ieee.org Abstract Isomorphic Graph Representations: This poster presents a feature level fusion of face and palmprint biometrics. It uses the improved K-medoids clustering algorithm and isomorphic graph. The performance of the system has been verified by two distance metrics namely, K-NN and normalized correlation metrics. It uses two multibiometrics databases of face and palmprint images for testing. The experimental results reveal that the feature level fusion with the improved K-medoids partitioning algorithm Exhibits robust performance and increases its performance with utmost level of accuracy. Steps: • Detection and localization of face and palm image • Extraction of SIFT feature points from face and palmprint images • Partitioning the SIFT points • Establishing correspondence between feature points Fusion of Keypoints: • Isomorphic graph representations • Fusion of matching keypoints • Matching • K-Nearest Neighbor • Correlation distance SIFT Points Extraction: Experimental Results: SIFT Features Extraction from Face and Palmprint Images . SIFT Points Clustering using Improve K-Medoids Algorithm: Step 1: Select randomly k number of points from the SIFT points set as the medoids. Step 2: Assign each SIFT feature point to the closest medoid which can be defined by a distance metric (i.e., Minkowski distance over the Euclidean space) Step 3: for each medoid i, i = 1, 2…k for each non-medoid SIFT point j swap i and j and compute the total cost of the configuration Step 4: Select the configuration with the lowest cost Step 5: Repeat Step 2 to Step 5 until there is no change in the medoid. Improved version of PAM clustering using Silhouette approximations: ( y (i ) + y (i + 1)) / 2 − ( x (i ) + x (i + 1)) / 2 S (i ) = August 19, 2010 max[(( x (i ) + x (i + 1)), ( y (i ) + y (i + 1))]

×