Discriminant Action Representation for View-Invariant Person Identification

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


    In this paper we propose a novel person identification method exploiting human motion information. Persons are described by using their poses during action execution. Identification process involves Fuzzy Vector Quantization and Discriminant Learning. In the case of multiple cameras used in the identification phase, single-view identification results combination is achieved by employing a Bayesian combination strategy. The proposed identification approach does not set the assumptions of known action class and number of capturing cameras in the identification phase. Experimental results on two publicly available video databases denote the effectiveness of the proposed approach.
    Original languageEnglish
    Title of host publication2012 19th IEEE International Conference on Image Processing (ICIP)
    Publication statusPublished - 2012
    Publication typeA4 Article in conference proceedings


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