In this paper, we discuss the problem of estimating the pose of an automobile driver from video of the driver as he or she drives the vehicle. The results reported are a follow-on to those presented in the IEEE Intelligent Transportation Systems Conference 2000 by the same authors. The previous results pertained to pose classification using a non-parametric eigenface approach. Although the eigenface approach yielded impressive results, there were certain types of mis-classification errors that could be eliminated perhaps by using a different approach. In this paper, classification results obtained by another non-parametric approach, namely Fisherfaces, are compared with the eigenface approach. These results show that Fisherfaces outperform eigenfaces.


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    Titel :

    Comparison between eigenfaces and Fisherfaces for estimating driver pose


    Beteiligte:
    Lakshmanan, S. (Autor:in) / Watta, P. (Autor:in) / Yu Lin Hou, (Autor:in) / Gandhi, N. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    498929 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Comparison Between Eigenfaces and Fisherfaces for Estimating Driver Pose

    Lakshmanan, S. / Watta, P. / Hou, Y. L. et al. | British Library Conference Proceedings | 2001


    Combined Fisherfaces framework

    Yang, J. / Yang, J. y. / Frangi, A. F. | British Library Online Contents | 2003


    Eigenfaces for Recognition

    Turk, M. A. | British Library Online Contents | 1990


    An Eigenface Approach for Estimating Driver Pose

    Watta, P. / Gandhi, N. / Lakshmanan, S. et al. | British Library Conference Proceedings | 2000


    An eigenface approach for estimating driver pose

    Watta, P. / Gandhi, N. / Lakshmanan, S. | IEEE | 2000