Linear discriminant analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper; a new nonparametric linear feature extraction method, stepwise nonparametric margin maximum criterion (SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the non- singularity of the within-class scatter matrix neither. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Face recognition by stepwise nonparametric margin maximum criterion


    Beteiligte:
    Xipeng Qiu, (Autor:in) / Lide Wu, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    600947 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Face Recognition by Stepwise Nonparametric Margin Maximum Criterion

    Qiu, X. / Wu, L. / IEEE | British Library Conference Proceedings | 2005


    Nonparametric Maximum Margin Criterion for Face Recognition

    Qiu, X. / Wu, L. | British Library Conference Proceedings | 2005



    Nonlinear face recognition based on maximum average margin criterion

    Baochang Zhang, / Xilin Chen, / Shiguang Shan, et al. | IEEE | 2005


    Discriminant feature extraction for image recognition using complete robust maximum margin criterion

    Chen, X. / Cai, Y. / Chen, L. et al. | British Library Online Contents | 2015