Combining face and iris biometrics for identity verification

  • Authors:
  • Yunhong Wang;Tieniu Tan;Anil K. Jain

  • Affiliations:
  • National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Department of Computer Sciences Engineering, Michigan State University, East Lansing, MI

  • Venue:
  • AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
  • Year:
  • 2003

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Abstract

Face and iris identification have been employed in various biometric applications. Besides improving verification performance, the fusion of these two biometrics has several other advantages. We use two different strategies for fusing iris and face classifiers. The first strategy is to compute either an unweighted or weighted sum and to compare the result to a threshold. The second strategy is to treat the matching distances of face and iris classifiers as a two-dimensional feature vector and to use a classifier such as Fisher's discriminant analysis and a neural network with radial basis function (RBFNN) to classify the vector as being genuine or an impostor. We compare the results of the combined classifier with the results of the individual face and iris classifiers.