An Efficient Density-based clustering algorithm for face groping

This paper focuses on the following problem: Given a large number of unlabeled face images, group them into individual clusters, and the number of clusters cannot be known in advance. To this end, an Efficient Density-based clustering incorporated with the model of Graph partitioning (EDG) is proposed. 1. Inspired by the progress of graph partitioning clustering, a novel criterion that can be seen as a variant of the Normalized-cut model is employed to measure the similarity between two samples. 2. We only consider the similarities and connections on a subset of all possible pairs, i.e. the top-K nearest neighbors for each sample. Therefore, the computing and storage costs are linear w.r.t. the number of samples. In order to assess the performance of EDG on face images, extensive experiments based on a two-stage framework have been conducted on 19 benchmark datasets (14 middle-scale and 5 large-scale) from the literature. The experimental results have shown the effectiveness and robustness of our model, compared with the state-of-the-art methods.