Investigations that require the exploitation of large volumes of face imagery are increasingly common
in current forensic scenarios (e.g., Boston Marathon bombing), but effective solutions for triaging such
imagery (i.e., low importance, moderate importance, and of critical interest) are not available in the
literature. General issues for investigators in these scenarios are a lack of systems that can scale to
volumes of images over 100K, and a lack of established methods for clustering the face images into the
unknown number of persons of interest contained. As such, we explore best practices for clustering
large sets of face images into large numbers of clusters as a method of reducing the volume of data to
be investigated by forensic analysts (Fig. 1).
|Fig. 1 Semi-automated investigative workflow, leveraging clustering.
1. C. Otto, B. Klare, and A. K. Jain, "An Efficient Approach for Clustering Face Images", ICB, Phuket, Thailand, May 19-22, 2015.[pdf]