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In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i.e., automatic subspace learning) and 2) how to learn the underlying subspace in the presence of Gaussian noise (i.e., robust subspace learning). We show that these two problems can be simultaneously solved by proposing a new method...
Estimating 3-dimensional head pose from a single 2D image is a challenging task with extensive applications. Existing approaches lack the capability to deal with multiple pose-related and — unrelated factors in a uniform way. Most of them can provide only 1-dimensional yaw estimation and suffer from limited representation ability for out-of-sample testing inputs. These drawbacks limit their performance...
Head pose estimation from images is a challenging task with extensive applications. It has been attracting research attentions over decades and numerous approaches have been proposed. Among them, manifold embedding based methods, which assume that the pose variations lie on a low-dimensional manifold embedded in the high-dimensional feature space, have achieved great success. However, previous manifold...
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