This digital humanities project consists of several parts. The main part is to be able to do face recognition among historical photographs, mainly from late 1800 to early 1900. The idea is to later be able to see whether additional photos of a person can help identifying that person as already existing in the database. Historical photos are in many ways different from modern photos. In this continuation of the project we have already looked at how different parts of the pipeline works in different packes and what combinations will give the best recognition accuracy known as mean average precision. We now need to parallelise at least three different pipelines and run them on about 300.000 face images. In the next step we will investigate how dimensionality reduction techniques (t-SNE UMAP etc) can be used to visualise the data and also investigate what the spatial distribution actually means. We have already concluded that women and men can be separated rather well, but we still do not know yet if age etc can have a similar impact. In any case, we need to do a lot of computing using CPU's rather than learning on GPU's since we will do no learning at this stage.