Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2013.6738697
Title: A sparse sampling model for 3D face recognition
Authors: Yuan, J.
Kassim, A.A. 
Keywords: 3D face recognition
feature selection
LDA
sparse sampling
Issue Date: 2013
Source: Yuan, J.,Kassim, A.A. (2013). A sparse sampling model for 3D face recognition. 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings : 3381-3385. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2013.6738697
Abstract: We propose a sparse sampling model as a feature selection tool for 3D face recognition, and compare its performance with the traditional dense subspace methods. The sparse LDA algorithm is applied to find the most discriminative features on range and texture images, meanwhile achieving the region selection purpose. The selected regions from both shape and texture are demonstrated. The classification remains accurate even at a high level of sparsity. To generalize the model, a probability density function is then estimated according to the selected region, and new samples are drawn accordingly to form new sparse features for classification. We also use the local coordinate system to make the sampling process more efficient, and insensitive to geometric transforms. © 2013 IEEE.
Source Title: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/83425
ISBN: 9781479923410
DOI: 10.1109/ICIP.2013.6738697
Appears in Collections:Staff Publications

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