Matching Tensors for Pose Invariant Automatic 3D Face Recognition

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    11 Citations (Scopus)


    The face is an easily collectible and non-intrusive biometric used for the authentication and identification of individuals. 2D face recognition techniques are sensitive to changes in illumination, makeup and pose. We present a fully automatic 3D face recognition algorithm that overcomes these limitations. During the enrollment, 3D faces in the gallery are represented by third order tensors which are indexed by a 4D hash table. During online recognition, tensors are computed for a probe and are used to cast votes to the tensors in the gallery using the hash table. Gallery faces are ranked according to their votes and a similarity measure based on a linear correlation coefficient and registration error is calculated only for the high ranked faces. The face with the highest similarity is declared as the recognized face. Experiments were performed on a database of 277 subjects and a rank one recognition rate of 86.4% was achieved. Our results also show that our algorithm’s execution time is insensitive to the gallery size.
    Original languageEnglish
    Title of host publicationConference on Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society
    EditorsCordelia Schmid, Stefano Soatto, Carlo Tomasi
    Place of PublicationLos Alamitos, California, USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    ISBN (Print)0636919
    Publication statusPublished - Jun 2005
    EventMatching Tensors for Pose Invariant Automatic 3D Face Recognition - San Diego, California, USA
    Duration: 1 Jan 2005 → …


    ConferenceMatching Tensors for Pose Invariant Automatic 3D Face Recognition
    Period1/01/05 → …


    Dive into the research topics of 'Matching Tensors for Pose Invariant Automatic 3D Face Recognition'. Together they form a unique fingerprint.

    Cite this