Abstract
In this paper, we consider the problem of grouping a
collection of unconstrained face images in which the number of subjects is not known. We propose an unsupervised
clustering algorithm called Deep Density Clustering (DDC)
which is based on measuring density affinities between local
neighborhoods in the feature space. By learning the minimal covering sphere for each neighborhood, information
about the underlying structure is encapsulated. The encapsulation is also capable of locating high-density region of
the neighborhood, which aids in measuring the neighborhood similarity. We theoretically show that the encapsulation asymptotically converges to a Parzen window density
estimator. Our experiments show that DDC is a superior
candidate for clustering unconstrained faces when the number of subjects is unknown. Unlike conventional linkage and
density-based methods that are sensitive to the selection operating points, DDC attains more consistent and improved
performance. Furthermore, the density-aware property reduces the difficulty in finding appropriate operating points