A Compact Discriminative Representation for Efficient Image-set Classification with Application to Biometric Recognition

M. Uzair, Arif Mahmood, Ajmal Mian, Chris Mcdonald

    Research output: Chapter in Book/Conference paperConference paper

    12 Citations (Scopus)
    399 Downloads (Pure)

    Abstract

    We present a simple yet compact and discriminative representation for image sets which can efficiently be used for image-set based object classification. For each image-set we compute a global covariance matrix which captures correlated variations in all image-set dimensions. Without loss of information, we compact the covariance matrix into a lower triangular matrix by using Cholesky decomposition. While preserving discrimination capability of the representation, we obtain further compression by applying Multiple Discriminant Analysis. As a result, we are able to represent image sets containing N samples each of dimensionality d by a single vector whose dimensionality is ≪ N d. We apply the proposed representation to various biometric applications such as image-set based face recognition and person identification using image-sets of periocular regions. To show that our representation is generic, we also report results for image-set based object categorization. We observe improved accuracy and significant speedup over the current state-of-the-art techniques on standard datasets.
    Original languageEnglish
    Title of host publicationProceedings of the 2013 International Conference on Biometrics (ICB)
    Place of PublicationMadrid, Spain
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-8
    ISBN (Print)9781479903108
    DOIs
    Publication statusPublished - Jun 2013
    Event2013 International Conference on Biometrics - Madrid, Spain, Madrid , Spain
    Duration: 4 Jun 20137 Jun 2013

    Conference

    Conference2013 International Conference on Biometrics
    CountrySpain
    CityMadrid
    Period4/06/137/06/13

    Fingerprint Dive into the research topics of 'A Compact Discriminative Representation for Efficient Image-set Classification with Application to Biometric Recognition'. Together they form a unique fingerprint.

    Cite this