GSoC/2019/StatusReports/ThanhTrungDinh
digiKam AI Face Recognition with OpenCV DNN module
digiKam is KDE desktop application for photos management. For a long time, digiKam team has put a lot of efforts to develop face engine, a feature allowing to scan user photos and suggest face tags automatically basing on pre-tagged faces 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, combining with highly-optimized OpenCV DNN module.
The project includes 2 main parts:
- Improve face recognition: implementation with OpenCV DNN module
- reduce processing time while keeping high accuracy
- classify unknown faces into classes of similar faces
- Improve face detection: implementation to be investigated
- detect faces across various scales (e.g. big, small, etc.), with occlusion (e.g. sunglasses, scarf, mask etc.), with different orientations (e.g. up, down, left, right, side-face etc.)
Mentors : Maik Qualmann, Gilles Caulier, Stefan Müller
Work report
Bonding period (May 6 to May 27)
Coding period : Phase one (May 28 to June 23)
Important Links
Proposal Link
Git dev branch
Contribution
Contacts
Email: [email protected]
Github: TrungDinhT