Open source photo management software digiKam has been updated to version 7.0.0, an update that has been a full year in the making. The updated digiKam adds a number of new features and bug fixes, according to the team behind the software, including an important update to the software's face-detection capability, which now utilizes deep learning technology to better detect both human and animal faces.

The neural network model now used for digiKam's face detection feature can detect faces in a variety of states and arrangements, including profile shots, burred faces, faces partially obstructed by other objects like sunglasses and more. The digiKam team says that testing the updated feature with 'huge collections' revealed a high level of accuracy resulting in few false positives.

Though the workflow for this updated face recognition function remains the same, the digiKam team notes that users will need to train the neural network to recognize the faces of the people they often photograph by tagging them in multiple images. In cases where the algorithm isn't trained, detected faces are instead marked as 'unknown.'

Other improvements are coming to the face detection tool and related workflow, as well. Users will soon be able to tell digiKam to ignore certain faces using the Reject button. The software will pin Important face tags in the sidebar and automatically assign face tag icons for a faster, more visual workflow. These features, as well as a handful of others, are being worked on over this summer.

Another project underway for digiKam is improvements to the software's face recognition neural network engine. The algorithm will be updated to implement new face classifiers for faster and more accurate facial recognition, plus it will receive a new face embedding database and more. The code with these changes is expected to arrive sometime this summer, according to the team, which says that'll likely drop with digiKam version 7.2.0.

Beyond those face recognition changes and planned changes, digiKam 7.0.0 brings LibRaw 0.20, the new version of this library that enables the software to post-process a variety of raw camera files.

With this update, digiKam has gained support for more than 40 additional raw image formats, including ones from cameras like the Sony A7R4, DJI Mavic Air, Ricoh GR III, PhaseOne IQ4 150MP, GoPro HERO7, and more, including several smartphone models. Users can find the full list of supported formats, including the 0.20 additions, on the LibRaw website.

The changes in digiKam 7.0.0 continue from there, including the addition of improved support for the HEIF image format via the use of the libheif shared library, new support for Microsoft Visual C++ with the goal of an eventual Windows Store release, official support for FlatPak Linux bundle, expanded metadata options, a new 'HTML5Responsive' theme for the HTMLGallery plugin, new settings for the SlideShow tool and much more.

Ultimately, the digiKam team explains that over the past year, they reached a new and 'impressive' level of development for the software beyond what they have achieved in the past. The update is extensive, adding considerable support and new features to what remains a very powerful open-source alternative to commercial photo management software.

As expected, digiKam 7.0.0 is available to download for free with support for Windows, macOS and Linux.