GSoC/2019/StatusReports/ThanhTrungDinh: Difference between revisions
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= 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. | 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. | ||
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== Work report == | == Work report == | ||
=== Bonding period (May 6 to May 27) | === Bonding period (May 6 to May 27) === | ||
=== Coding period : Phase one (May 28 to June 23) === | === Coding period : Phase one (May 28 to June 23) === | ||
Revision as of 12:51, 23 June 2019
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)
Coding period : Phase one (May 28 to June 23)
Important Links
Proposal Link
Git dev branch
Contribution
Contacts
Email: [email protected]
Github: TrungDinhT