GSoC/2019/StatusReports/ThanhTrungDinh

digiKam AI Face Recognition with OpenCV DNN module

digiKam is a well-known 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. Being aware of that, digiKam team has put a lot of efforts to develop face engine, which scan scan photos and suggest 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, in order to bring this wonderful functionality back to users in a very soon release.

Mentors : Maik Qualmann, Gilles Caulier, Stefan Müller

Project Goals

• Implement DNN based approach and unit tests with OpenCV DNN module
• Complete integration tests on computational and accuracy benchmark for face engine
• Study performance metrics and decide which algorithm and which kind of neural network architecture to use for facial recognition in digiKam
• (Optionally) Implement facial detection with OpenCV DNN module to replace current method using Haar cascade algorithm

Work report

=== Bonding period (May 6 to May 27)

Important Links

Proposal Link

Project Proposal

Git dev branch

gsoc19-face-recognition

Contacts

Email: [email protected]

Github: TrungDinhT