This paper discusses a multimodal biometric sensor fusion approach for controlling building access. The motivation behind using multimodal biometrics is to improve universality and accuracy of the system. A Bayesian framework is implemented to fuse the decisions received from multiple biometric sensors. The system accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase or reduce the security level. This is important to systems designed for varying security needs and user access requirements. The additional biometric modes and variable error costs give the system adaptability improving system acceptability. This paper presents the framework using three different biometric systems: voice, face, and hand biometric systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving personal identification accuracy using multisensor fusion for building access control applications


    Beteiligte:
    Osadciw, L. (Autor:in) / Varshney, P. (Autor:in) / Veeramachaneni, K. (Autor:in)


    Erscheinungsdatum :

    01.01.2002


    Format / Umfang :

    404444 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Improving Personal Identification Accuracy Using Multisensor Fusion for Building Access Control Applications

    Osadciw, L. / Varshney, P. / Veeramacheneni, K. et al. | British Library Conference Proceedings | 2002


    Distributed Multisensor Fusion

    Pao, L. / AIAA | British Library Conference Proceedings | 1994


    Omega Improves Multisensor Accuracy

    Miller, B. | Tema Archiv | 1972


    Distributed multisensor fusion

    Pao, Lucy | AIAA | 1994


    Multisensor data fusion

    Varshney, P.K. | Tema Archiv | 1997