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Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using k-nearest neighborhood (k-NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-known...
This paper contrasts two approaches to facial landmarking in 3D. The first approach is statistical in nature, and is based on modeling the shape of each feature with Gaussian mixtures. The advantage of this approach is the uniform treatment of landmarks. The second approach is a hybrid method to find the nose tip, which does not require learning, and is robust under adverse conditions. We demonstrate...
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