Activity based Person Identification using Fuzzy Representation and Discriminant Learning

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

    Research output: Contribution to journalArticleScientificpeer-review

    46 Citations (Scopus)


    In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which ’walk’ (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy Vector Quantization and Linear Discriminant Analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
    Original languageEnglish
    Pages (from-to)530-542
    Number of pages12
    JournalIEEE Transactions on Information Forensics and Security
    Issue number2
    Publication statusPublished - 2012
    Publication typeA1 Journal article-refereed


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