We introduce a novel discriminative feature space which is efficient not only for face detection but also for recognition. The face representation is based on local binary patterns (LBP) and consists of encoding both local and global facial characteristics into a compact feature histogram. The proposed representation is invariant with respect to monotonic gray scale transformations and can be derived in a single scan through the image. Considering the derived feature space, a second-degree polynomial kernel SVM classifier was trained to detect frontal faces in gray scale images. Experimental results using several complex images show that the proposed approach performs favorably compared to the state-of-the-art methods. Additionally, experiments with detecting and recognizing low-resolution faces from video sequences were carried out, demonstrating that the same facial representation can be efficiently used for both detection and recognition.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A discriminative feature space for detecting and recognizing faces


    Beteiligte:
    Hadid, A. (Autor:in) / Pietikainen, M. (Autor:in) / Ahonen, T. (Autor:in)


    Erscheinungsdatum :

    2004-01-01


    Format / Umfang :

    654044 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Discriminative Feature Space for Detecting and Recognizing Faces

    Hadid, A. / Pietikainen, M. / Ahonen, T. et al. | British Library Conference Proceedings | 2004


    Recognizing faces with PCA and ICA

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


    Fast Feature-Recognizing Optoelectronic System

    Thakoor, S. / Thakoor, A. P. | NTRS | 1990


    Recognizing semantic correlation in image-text weibo via feature space mapping

    Liu, Maofu / Zhang, Luming / Liu, Ya et al. | British Library Online Contents | 2017