Using context information and local feature points in face clustering for consumer photos

We introduce local feature points to achieve face clustering for consumer photos. After combining eigenfaces with context information like clothes, we further investigate the usage of local feature points to match face images. The relationships between face images are constructed by feature matching and then described as a graph. Outliers in the results of preliminary clustering are detected and are re-clustered according to matching characteristics. We report complete performance comparison for different datasets and show that the proposed method has superior performance than conventional approaches.