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Classification with image sets is recently a compelling technique for video-based face recognition. Previous methods in this line mostly assume each image set is pure, i.e., containing well-aligned face images of the same subject, which however is hardly satisfied in real-world applications due to incorrect face detection, questionable tracking, or multiple faces in a single image. This paper proposes...
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...
In this paper, we proposed a novel semi-supervised classification method with path-based similarity measure for face recognition. Based on the manifold assumption, our method can reflect genuine similarities between data points on manifolds without any other additional knowledge, which takes into account the existence of noise and outliers in the face dataset. Comparison experiments between the proposed...
We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown class. We propose a low complexity solution that is able to exploit the properties of the data manifold with a graph-based algorithm. It results into...
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