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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.
|Title of host publication||Conference on Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society|
|Editors||Cordelia Schmid, Stefano Soatto, Carlo Tomasi|
|Place of Publication||Los Alamitos, California, USA|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - Jun 2005|
|Event||Matching Tensors for Pose Invariant Automatic 3D Face Recognition - San Diego, California, USA|
Duration: 1 Jan 2005 → …
|Conference||Matching Tensors for Pose Invariant Automatic 3D Face Recognition|
|Period||1/01/05 → …|
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