We present a system for integrating multiple sources of data for finding missing
persons. This system can assist authorities in finding children during amber alerts, mentally
challenged persons who have wandered off, or person-of-interests in an investigation.
Authorities search for the person in question by reaching out to acquaintances, checking video
feeds, or by looking into the previous histories relevant to the investigation. In the absence of
any leads, authorities lean on public help from sources such as tweets or tip lines. A missing
person investigation requires information from multiple modalities and heterogeneous data
sources to be combined.<div>Existing cross-modal fusion models use separate information models for each data modality and
lack the compatibility to utilize pre-existing object properties in an application domain. A
framework for multimodal information retrieval, called Find-Them is developed. It includes
extracting features from different modalities and mapping them into a standard schema for
context-based data fusion. Find-Them can integrate application domains with previously derived
object properties and can deliver data relevant for the mission objective based on the context
and needs of the user. Measurements on a novel open-world cross-media dataset show the
efficacy of our model. The objective of this work is to assist authorities in finding uses of
Find-Them in missing person investigation.</div>