Fuzzy Normalisation Methods for Pattern Verification

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    A fuzzy approach to normalisation methods for pattern verification is presented in this paper. For an input token and a claimed identity, a score is calculated and compared with a given threshold to accept or reject the claimed person. The score is regarded as a fuzzy memebership function in this approach. The paper shows how to find effective scores to reduce both false rejection and false acceptance errors. Speaker verification experiments performed on the ANDOSL database and utterance verification experiments performed on the TI46 database showed better results for fuzzy methods
    Original languageEnglish
    Title of host publicationInternational Conference on Biometric Authentication, ICBA 2004
    EditorsD Zhang, AK Jain
    Place of PublicationBerlin Heidelberg, Germany.
    PublisherSpringer
    Pages648-654
    Number of pages7
    ISBN (Print)9783540221463
    DOIs
    Publication statusPublished - 2004
    EventInternational Conference on Biometric Authentication - , Hong Kong
    Duration: 15 Jul 200417 Jul 2004

    Conference

    ConferenceInternational Conference on Biometric Authentication
    CountryHong Kong
    Period15/07/0417/07/04

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  • Cite this

    Tran, D. (2004). Fuzzy Normalisation Methods for Pattern Verification. In D. Zhang, & AK. Jain (Eds.), International Conference on Biometric Authentication, ICBA 2004 (pp. 648-654). Springer. https://doi.org/10.1007/978-3-540-25948-0_88