We develop a pairwise classification framework for face recognition, in which a C class face recognition problem is divided into a set of C(C-1)/2 two class problems. Such a problem decomposition not only leads to a set of simpler classification problems to be solved, thereby increasing overall classification accuracy, but also provides a framework for independent feature selection for each pair of classes. A simple feature ranking strategy is used to select a small subset of the features for each pair of classes. Furthermore, we evaluate two classification methods under the pairwise comparison framework: the Bayes classifier and the AdaBoost. Experiments on a large face database with 1079 face images of 137 individuals indicate that 20 features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pairwise face recognition


    Beteiligte:
    Guo-Dong Guo, (Autor:in) / Hong-Jiang Zhang, (Autor:in) / Li, S.Z. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    584998 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pairwise Face Recognition

    Guo, G. / Zhang, H. / Li, S. et al. | British Library Conference Proceedings | 2001




    Pairwise Coupling for Machine Recognition of Hand-Printed Japanese Characters

    Roth, V. / Tsuda, K. / IEEE | British Library Conference Proceedings | 2001


    Beyond pairwise clustering

    Agarwal, S. / Jongwoo Lim, / Zelnik-Manor, L. et al. | IEEE | 2005