University of Sassari - Computer Vision Laboratory
Doi
Abstract
Multi-biometrics has recently emerged as a mean of more robust and effcient
personal verification and identification. Exploiting information from multiple
sources at various levels i.e., feature, score, rank or decision, the false acceptance
and rejection rates can be considerably reduced. Among all, feature level fusion
is relatively an understudied problem. This paper addresses the feature level
fusion for multi-modal and multi-unit sources of information. For multi-modal
fusion the face and iris biometric traits are considered, while the multi-unit fusion
is applied to merge the data from the left and right iris images. The proposed
approach computes the SIFT features from both biometric sources, either multi-
modal or multi-unit. For each source, the extracted SIFT features are selected via
spatial sampling. Then these selected features are finally concatenated together
into a single feature super-vector using serial fusion. This concatenated feature
vector is used to perform classification.
Experimental results from face and iris standard biometric databases are
presented. The reported results clearly show the performance improvements in
classification obtained by applying feature level fusion for both multi-modal and
multi-unit biometrics in comparison to uni-modal classification and score level
fusion