A 3D face matching framework

Many 3D face matching techniques have been developed to perform face recognition. Among these techniques are variants of 3D facial curve matching, which are techniques that reduce the amount of face data to one or a few 3D curves. The face's central profile, for instance, proved to work well. H...

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Bibliographic Details
Published in2008 IEEE International Conference on Shape Modeling and Applications pp. 103 - 110
Main Authors ter Haar, F.B., Veltkamp, R.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Data mining
Face detection
Face recognition
H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-Retrieval models
Head
Information retrieval
Iterative algorithms
Iterative closest point algorithm
Nose
Robustness
Shape
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Summary:Many 3D face matching techniques have been developed to perform face recognition. Among these techniques are variants of 3D facial curve matching, which are techniques that reduce the amount of face data to one or a few 3D curves. The face's central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them is still underexposed. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in literature, and select subsets of facial curves for effective and efficient face matching. Results on the 3D face retrieval track of SHREC'07 (the 3D SHape Retrieval Contest) shows the highest mean average precision achieved so far, using only three facial curves of 45 samples each.
ISBN:9781424422609
1424422604
DOI:10.1109/SMI.2008.4547956