digiKam is KDE desktop application for photos management. In digiKam, tags on photos are strongly supported for the sake of providing users with a natural workflow of searching and arranging photos in their collections. Since many of our photos contain faces, face tag has apparently emerged as an essential property for any photos management software, also with the fact that user has to tag thousand of their photos manually. Being aware of that, digiKam team has put a lot of efforts to develop face engine, capable of scanning photos and suggesting face tags automatically basing on pre-tagged photos by users. However, that functionality is currently deactivated in digiKam, as it is slow while not adequately accurate. Thus, this project aims to improve the performance and accuracy of facial recognition in digiKam by exploiting state-of-the-art neural network models in AI and machine learning.



Thanh Trung Dinh


  • Maik Qualmann
  • Gilles Caulier
  • Stefan Müller