DocumentCode :
2861079
Title :
Matching Tensors for Pose Invariant Automatic 3D Face Recognition
Author :
Mian, A.S. ; Bennamoun, M. ; Owens, R.A.
Author_Institution :
University of Western Australia
fYear :
2005
fDate :
25-25 June 2005
Firstpage :
120
Lastpage :
120
Abstract :
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.
Keywords :
Authentication; Biometrics; Computer science; Databases; Face recognition; Lighting; Probes; Software engineering; Tensile stress; Voting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on
Conference_Location :
San Diego, CA, USA
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.490
Filename :
1565432
Link To Document :
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