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- W2736953706 abstract "In this paper we propose a new, unsupervised approach to multi-view face recognition, which can be formulated mathematically as a problem of partitioning of pairwise proximity data, obtained from multi-view face image sequences. The proposed approach is implemented in two steps: in the first step, the so-called VQ-faces are calculated incrementally as prototype vectors of local areas in image-space, coding for different face-views (i.e. a view code-book is generated). In the second step the different face sequences are clustered into identity categories by a combinatorial optimization-based partitioning of the proximity matrix of pairwise relations between the sequences, generated with respect to the VQ-faces. The general approach proposed here has been tested experimentally on a data set of multi-view face sequences gathered over a period of several months in real-world conditions and encouraging results were obtained" @default.
- W2736953706 created "2017-07-31" @default.
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- W2736953706 date "2004-01-01" @default.
- W2736953706 modified "2023-09-24" @default.
- W2736953706 title "VQ-faces - Unsupervised Multi-View Face Recognition by Pairwise Clustering" @default.
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