22 research outputs found
Feature Level Fusion of Face and Fingerprint Biometrics
The aim of this paper is to study the fusion at feature extraction level for
face and fingerprint biometrics. The proposed approach is based on the fusion
of the two traits by extracting independent feature pointsets from the two
modalities, and making the two pointsets compatible for concatenation.
Moreover, to handle the problem of curse of dimensionality, the feature
pointsets are properly reduced in dimension. Different feature reduction
techniques are implemented, prior and after the feature pointsets fusion, and
the results are duly recorded. The fused feature pointset for the database and
the query face and fingerprint images are matched using techniques based on
either the point pattern matching, or the Delaunay triangulation. Comparative
experiments are conducted on chimeric and real databases, to assess the actual
advantage of the fusion performed at the feature extraction level, in
comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc
Ear Identification by Fusion of Segmented Slice Regions using Invariant Features: An Experimental Manifold with Dual Fusion Approach
This paper proposes a robust ear identification system which is developed by
fusing SIFT features of color segmented slice regions of an ear. The proposed
ear identification method makes use of Gaussian mixture model (GMM) to build
ear model with mixture of Gaussian using vector quantization algorithm and K-L
divergence is applied to the GMM framework for recording the color similarity
in the specified ranges by comparing color similarity between a pair of
reference ear and probe ear. SIFT features are then detected and extracted from
each color slice region as a part of invariant feature extraction. The
extracted keypoints are then fused separately by the two fusion approaches,
namely concatenation and the Dempster-Shafer theory. Finally, the fusion
approaches generate two independent augmented feature vectors which are used
for identification of individuals separately. The proposed identification
technique is tested on IIT Kanpur ear database of 400 individuals and is found
to achieve 98.25% accuracy for identification while top 5 matched criteria is
set for each subject.Comment: 12 pages, 3 figure