When data goes missing: methods for missing score imputation in biometric fusion

Yaohui Ding; Arun Ross

doi:10.1117/12.851345

14 April 2010 When data goes missing: methods for missing score imputation in biometric fusion

Yaohui Ding, Arun Ross

Proceedings Volume 7667, Biometric Technology for Human Identification VII; 76670V (2010) https://doi.org/10.1117/12.851345
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

While fusion can be accomplished at multiple levels in a multibiometric system, score level fusion is commonly used as it offers a good trade-off between fusion complexity and data availability. However, missing scores affect the implementation of several biometric fusion rules. While there are several techniques for handling missing data, 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. This paper compares the performance of three imputation methods: Imputation via Maximum Likelihood Estimation (MLE), Multiple Imputation (MI) and Random Draw Imputation through Gaussian Mixture Model estimation (RD GMM). A novel method called Hot-deck GMM is also introduced and exhibits markedly better performance than the other methods because of its ability to preserve the local structure of the score distribution. Experiments on the MSU dataset indicate the robustness of the schemes in handling missing scores at various missing data rates.

Citation Download Citation

Yaohui Ding and Arun Ross "When data goes missing: methods for missing score imputation in biometric fusion", Proc. SPIE 7667, Biometric Technology for Human Identification VII, 76670V (14 April 2010); https://doi.org/10.1117/12.851345

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