English
资源说明：Abstract—Clustering face images according to their latent
identity has two important applications: (i) grouping a collection
of face images when no external labels are associated with images,
and (ii) indexing for efficient large scale face retrieval. The
clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the
partition algorithm. We first propose a representation based on
ResNet, which has been shown to perform very well in image
classification problems. Given this representation, we design a
clustering algorithm, Conditional Pairwise Clustering (ConPaC),
which directly estimates the adjacency matrix only based on the
similarities between face images. This allows a dynamic selection
of number of clusters and retains pairwise similarities between
faces. ConPaC formulates the clustering problem as a Conditional
Random Field (CRF) model and uses Loopy Belief Propagation
to find an approximate solution for maximizing the posterior
probability of the adjacency matrix. Experimental results on two
benchmark face datasets (LFW and IJB-B) show that ConPaC
outperforms well known clustering algorithms such as k-means,
spectral clustering and approximate Rank-order. Additionally,
our algorithm can naturally incorporate pairwise constraints to
work in a semi-supervised way that leads to improved clustering
performance. We also propose an k-NN variant of ConPaC, which
has a linear time complexity given a k-NN graph, suitable for
large datasets.
Index Terms—face clustering, face representation, Conditional
Random Fields, pairwise constraints, semi-supervised clustering.
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