Unsupervised recognition of multi-view face sequences based on pairwise clustering with attraction and repulsion
In this paper we propose and investigate the possibilities inherent in a new, unsupervised approach to multi-view face recognition, which can be formulated mathematically as a problem of partitioning of proximity data, obtained from multi-view face image sequences. The proposed approach is implemented in two novel pairwise clustering algorithms, CAR1 and CAR2, which partition the input data into identity clusters by performing combinatorial optimization guided by two types of interaction forces, attraction and repulsion, imposed on the original proximity matrices. Several experiments were conducted in order to test the performance of the proposed algorithms on real-world datasets including both frontal and side-view faces, which have been gathered over a period of several months. The obtained results can be considered encouraging for the general approach proposed here, and the new algorithms compared favorably to two other pairwise clustering algorithms, recently proposed in the image segmentation literature.
pairwise clustering multi-view face recognition image sequences combinatorial optimization unsupervised learning