Using density based score fusion for multimodal identification systems under the missing data scenario

Quang Duc Tran, Panos Liatsis, Bing Zhu, Changzheng He

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

While biometric fusion is a well-studied problem, most of fusion schemes cannot account for missing data (incomplete score lists), that is commonly encountered in large-scale multimodal identification systems. In this paper, we present a new approach, where RIBG (Robust Imputation Based on Group method of data handling) is used for handling the missing data. Since this scheme can be followed by a standard fusion approach designed for complete data, we propose a density based score fusion to achieve optimal performance in biometric systems. The rank-1 recognition rates of the proposed approach were 95.02% on the NIST-Multimodal database, 76.23% on NIST-Face database and 82.24% on NIST-Fingerprint database, even when the missing rate is set to 25%, which is higher than traditional approaches such as majority voting.

Original languageBritish English
Pages238-242
Number of pages5
DOIs
StatePublished - 2011
Event4th International Conference on Developments in eSystems Engineering, DeSE 2011 - Dubai, United Arab Emirates
Duration: 6 Dec 20118 Dec 2011

Conference

Conference4th International Conference on Developments in eSystems Engineering, DeSE 2011
Country/TerritoryUnited Arab Emirates
CityDubai
Period6/12/118/12/11

Keywords

  • Gaussian mixture model
  • Majority voting
  • Multimodal identification system
  • Robust imputation based on group method of data handling

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