Explainable face verification for video archive documentation

Film and video archives hold vast collections of historically relevant content, and the (co)appearance of specific persons is often one of the most interesting properties. However, while archive documentation does not always contain metadata which person is depicted in the content (and if so, where), it is often desired to obtain a list of persons related to the entire broadcast. We propose to obtain a set of sample images from web searches, and then perform a face verification task using an AI-based method to determine whether (and where) a person appears in the content. We analyse the influence of different annotation conditions and types of errors in automated face sample retrieval on the endtoend performance of the face verification process. To give the user insights into the success or failure of the verification, we apply a recent explainable face verification method. Thereby we investigate how the explainability performs on a completely different dataset.