Although important contributions to face recognition have been reported, few focus on how to robustly recognize expression variant faces from as few as one single training sample per class. Since learning cannot generally be applied when only one sample per class is available, matching techniques (distance measures) are usually employed instead (e.g. correlations). However, distance measures generally attempt to match all features with equal importance (weighting), because not only is it difficult to know which features are more useful (for classification), but when or under which circumstances this happens. For example, when recognizing faces in the original image space (e.g. using the Euclidean distance-correlation), it is not known which pixels are more and which are less appropriate for use. We use the optical flow between the testing and sample images as a measure of how good each pixel is. Pixels that have a small flow will have high weights, pixels with a large flow will have small weights. Our experimental results show that the method proposed in this contribution outperforms the classical Euclidean distance (correlation) measure and the PCA (principal component analysis) approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Recognizing expression variant faces from a single sample image per class


    Beteiligte:
    Martinez, A.M. (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    587452 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Recognizing Expression Variant Faces from a Single Sample Image per Class

    Martinez, A. / IEEE | British Library Conference Proceedings | 2003



    A Face Recognition System Dealing with Expression Variant Faces

    Arca, S. / Campadelli, P. / Lanzarotti, R. et al. | British Library Conference Proceedings | 2006


    Recognizing faces with PCA and ICA

    Draper, B. A. / Baek, K. / Bartlett, M. S. et al. | British Library Online Contents | 2003


    A discriminative feature space for detecting and recognizing faces

    Hadid, A. / Pietikainen, M. / Ahonen, T. | IEEE | 2004