The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Combining Face and Iris Biometrics for Identity Verification
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.
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A human identification technique using images of the iris and wavelet transform
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Efficient Iris Recognition by Characterizing Key Local Variations
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Biohashing: two factor authentication featuring fingerprint data and tokenised random number
Jin, A. T. B., Ling, D. N. C., & Goh, A. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.04.011 · Cited by 776
A Survey of Keystroke Dynamics Biometrics
Teh, P. S., Teoh, A. B. J., & Yue, S. (2013) · The Scientific World Journal · doi:10.1155/2013/408280 · Cited by 329
Anti-spoofing in Action: Joint Operation with a Verification System
Chingovska, I., Anjos, A., & Marcel, S. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2013.22 · Cited by 62
Metadata
- Authors
- Wang, Y., Tan, T., & Jain, A. K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2003
- Pages
- 805-813
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W2120433720
- Cited by
- 313
- References
- 24
- FWCI
- 14.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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