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A sparse sampling model for 3D face recognition

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.