CHAW POH, C., 2011. Multimodal biometrics score level fusion using non-confidence information. PhD, Nottingham Trent University.
203433_Chaw P Chia PhD Thesis 2011.pdf
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Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above.
|Creators:||Chaw Poh, C.|
|Rights:||This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the first instance to the owner(s) of the Intellectual Property Rights.|
|Divisions:||Schools > School of Science and Technology|
|Depositing User:||EPrints Services|
|Date Added:||09 Oct 2015 09:36|
|Last Modified:||09 Oct 2015 09:36|
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