GSoC/2021/StatusReports/NghiaDuong

Digikam: Faces engine improvements

digiKam is a famous open-source photo management software. Face engine is a tool helping users recognize and label faces in photos. Following the advance of Deep Learning, digiKam development team has been working on the Deep Learning implementation of the Faces engine since 2018. During the past few years, with the huge effort of digiKam developers and the great support from users, the Faces engine has been improved gradually.

Last year, during the 2020 Google Summer of Codes, I had a chance to work on digiKam's faces engine, as a part of the DNN based Faces Recognition Improvements project. At the end of this project, we were able to finish the implementation of a machine-learning-based classification system for facial recognition. On top of that, we also remodeled the face database of digiKam which was specialized in face embedding storage. As the final result, we achieved an accuracy of 84% on facial recognition, with a processing speed of about 19 ms/face.

However, after receiving reviews and bug reports from users, we found out that there are a few remaining problems on the faces engine. Therefore the main goals of this project to pick up the previous work and focus on improving the accuracy of digiKam's faces engine.

Mentors : Gilles Caulier, Maik Qualmann, Thanh Trung Dinh

Important Links

Project Proposal

Digikam Faces engine improvements

GitLab development branch

gsoc21-faces-engine

Contacts

Email: [email protected]

Github: MinhNghiaD

Invent KDE: minhnghiaduong

LinkedIn: https://www.linkedin.com/in/nghia-duong-2b5bbb15a/

Project Goals

The current goals of this project are to :

• Improve the accuracy of faces classifier with outlier detection
• Improve the speed of facial recognition and detection, improve batch processing
• Improve the face management pipeline organization