Using multi-instance enrollment to improve performance of 3D face recognition

Authors:
Timothy C. Faltemier;Kevin W. Bowyer;Patrick J. Flynn
Affiliations:
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Venue:
Computer Vision and Image Understanding
Year:
2008

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Abstract

This paper explores the use of multi-instance enrollment as a means to improve the performance of 3D face recognition. Experiments are performed using the ND-2006 3D face data set which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment approach outperforms a state-of-the-art component-based recognition approach, in which the face to be recognized is considered as an independent set of regions.