GSoC/2020/StatusReports/NghiaDuong

Digikam : DNN based Faces Recognition Improvements

DigiKam is a famous open-source photo management software. With a huge effort, the developers of digiKam have implemented face detection and facial recognition features in a module called faces engine. This module implements different methods to scan faces and then label them based on the pre-tagged photos given by users.

Since last year, as a result of Thanh Trung Dinh's project during GSoC 2019, digiKam's faces engine has adopted new CNN based face processing methods. These methods have been proven to give a better performance than other traditional image processing methods implemented in digiKam. However, there still are some limitations in the current implementation of the faces engine, therefore the main goals of this project to continue Thanh Trung Dinh's works and improve the performance of digiKam's faces engine.

Mentors : Gilles Caulier, Maik Qualmann, Thanh Trung Dinh

Important Links

Project Proposal

Digikam DNN based Faces Recognition Improvements

GitLab development branch

gsoc20-facesengine-recognition

Contacts

Email: [email protected]

Github: MinhNghiaD

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
• Optimize the use of memory of faces engine
• Decrease storage space of faces engine
• Improve processing speed
• Re-structure faces engine architecture
• Port faces engines to Plugin architecture

Work Report

Community Bonding period (May 1 to May 31)

During this period, my main objective was to familiarize myself with the work of Thanh Trung Dinh, in order to evaluate the current implementation.