ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering

Abstract

Clustering is an unsupervised technique for machine learning and data analysis. Different clustering methods such as centroid, connectivity, density, or distribution-based clustering have been applied as a step in many vision applications. Recently, face clustering has become an important task in the face recognition field, and evaluation benchmarks on the LFW and IJB-B datasets have been created. In this paper, we present the Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse (ECLIPSE) clustering algorithm, where we combine the advantages of centroid, density, and connectivity-based clustering algorithms. We show that ECLIPSE can work with most kinds of distance measures such as Euclidean, Cosine, and Bray-Curtis distance. We present the Alignment-Free Facial Feature Extraction (AFFFE) network to extract deep features for the LFW and IJB-B datasets. Using these features, our experimental results show that ECLIPSE can estimate the true number of clusters better than related algorithms and delivers state-of-the-art clustering results, especially for large datasets. Using only the hint in the IJB-B protocol, AFFFE and ECLIPSE significantly advance the state of the art.