Statistical analysis with missing data
Statistical analysis with missing data
Machine Learning - Special issue on learning with probabilistic representations
Robust Learning with Missing Data
Machine Learning
Machine Learning
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Imputation through finite Gaussian mixture models
Computational Statistics & Data Analysis
Handbook of Biometrics
Fusion in Multibiometric Identification Systems: What about the Missing Data?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Online learning in biometrics: a case study in face classifier update
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Score normalization in multimodal biometric systems
Pattern Recognition
IEEE Transactions on Information Forensics and Security
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hi-index | 0.01 |
Multibiometric systems, which consolidate or fuse multiple sources of biometric information, typically provide better recognition performance than unimodal systems. While fusion can be accomplished at various levels in a multibiometric system, score-level fusion is commonly used as it offers a good trade-off between data availability and ease of fusion. Most score-level fusion rules assume that the scores pertaining to all the matchers are available prior to fusion. Thus, they are not well equipped to deal with the problem of missing match scores. While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data. In this work, the performance of the following imputation methods are compared in the context of multibiometric fusion: K-nearest neighbor (KNN) schemes, likelihood-based schemes, Bayesian-based schemes and multiple imputation (MI) schemes. Experiments on the MSU database assess the robustness of the schemes in handling missing scores at different missing rates. It is observed that the Gaussian mixture model (GMM)-based KNN imputation scheme results in the best recognition accuracy.