August 19, 2010

Dakshina Ranjan Kisku, Phalguni Gupta, Jamuna Kanta SingDept. of CSE, Asansol Engineering College, Dept. of CSE, IIT Kanpur, Dept. of CSE, Jadavpur University, India

**Contact: drkisku@ieee.org

AbstractThis poster presents a feature level fusion of face and

palmprint biometrics. It uses the improved K-medoidsclustering 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 performancewith 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• Isomorphic graph representations

• Fusion of matching keypoints

• Matching

• K-Nearest Neighbor

• Correlation distance

SIFT Points Extraction:

.

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:

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2/))1()((2/))1()(()(

++++++−++=iyiyixix

ixixiyiyiS

Isomorphic Graph Representations:

Fusion of Keypoints:

Experimental Results: