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Face images are often very high-dimensional and complex. However, the actual underlying structure can be characterized by a small number of features. Hence, locally linear embedding (LLE) is proposed as a nonlinear dimension reduction technique to deal this problem. LLE learns the intrinsic manifold embedded in the high dimensional ambient space by minimizing the global reconstruction error of the...
In this paper, we propose a novel method for performing robust super-resolution of face images. Face super-resolution is to recover a high-resolution face image from a given low-resolution face image by modeling a face image space in view of multiple resolutions. The proposed method is based on the assumption that a low-resolution image space and a high-resolution image space have similar local geometries...
The estimation of head pose angle from face images is an integral component of face recognition systems, human computer interfaces and other human-centered computing applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional feature space. While manifold learning techniques capture the geometrical...
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