Combining Face and Iris Biometrics for Identity Verification

Face and iris identification have been employed in various biometric applications. Besides improving the verification performance, the fusion of both of the biometrics has several other advantages such as enlarging user population coverage and reducing enrollment failure. In this paper, we make a first attempt to combine face and iris biometrics. We use two different strategies for fusing iris and face classifiers. The first strategy is to compute either an unweighted or weighted sum of the two matching distances and compare the distances to a threshold. The second strategy is to treat the matching distances of face and iris classifiers as a two-dimensional feature vector and use a classifier such as the Fisher's discriminant analysis or 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. Experimental results show t...

• Type: Conference
• Year: 2003
• Where: AVBPA
• Authors: Yunhong Wang, Tieniu Tan, Anil K. Jain
• Keywords: AVBPA 2003, Face Classifiers, Iris Identification, Matching Distances