Robust Multi-modal and Multi-unit Feature Level Fusion of Face and Iris Biometrics

Abstract

Multi-biometrics has recently emerged as a mean of more robust and efficient 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 of 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, feature selection on the extracted SIFT features is performed via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated super 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.KeywordsFace ImageScale Invariant Feature TransformIris ImageLevel FusionIris RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.