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.


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    Title :

    Matching Tensors for Pose Invariant Automatic 3D Face Recognition


    Contributors:
    Mian, A.S. (author) / Bennamoun, M. (author) / Owens, R.A. (author)


    Publication date :

    2005-01-01


    Size :

    361881 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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