When digitizing historical archives, it is necessary to search for the faces of people, especially in newspapers, link them to the surrounding text, and make them searchable. Existing face detectors on datasets of scanned historical documents fail remarkably – current detection tools only achieve around 24 % mAP at 50:90 % IoU. This work compensates for this failure by introducing a new manually annotated domain-specific dataset in the style of the popular Wider Face dataset containing 2.2 k new images from digitized historical newspapers from the 19th to 20th century, with 11 k new bounding-box annotations and associated facial landmarks. This dataset enables existing detectors to be retrained, bringing their results closer to the standard in the field of face detection in the wild. We report several experimental results comparing different families of fine-tuned detectors with publicly available pre-trained face detectors. In ablation studies, we compare multiple detector sizes, providing comprehensive detection and landmark prediction performance results.