A comparison of imputation methods for handling missing scores in biometric fusion

Abstract

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. ► A study of various imputation methods for handling missing scores in multibiometrics. ► These methods can be followed by any standard score fusion scheme. ► Experiments highlight the potential of these methods in a multi-biometric system.