It was introduced for the same purposes as Eigen Faces.Īccording to rumours, this one is not finalized, it is said that not all methods are implemented. It was introduced to have a different source of results for face detection, enabling to proof the DNN approaches.Īnother algorithm what uses the OpenCV backend. It's not perfect and requires at least six faces already tagged manually by the user to identify the same faces in non-tagged images.Īn alternative algorithm what uses the OpenCV backend. Moreover, it is the oldest implementation of such an algorithm in digiKam. This is the most complete implementation of a face detection algorithm. OpenCV - Local Binary Patterns Histograms ( LBPH).This DNN is based on the Dlib implementation in OpenFace project. Deep Neural Network (DNN) Dlib C++ LibraryĭigiKam has already an experimental implementation of Neural Network to perform faces recognition what is rather proof of concept than a production-ready function.The 4 different methods are explained here in brief only, a more detailed description can be found in Digikam/GSoC2019/AIFaceRecognition The algorithms are complex but explained in more details in the wiki page for the GSoC faces recognition project. The goal is to be able to recognize automatically a non-tagged face from images, using previous face tags registered in the database. This introduces the four different methods based on different algorithms, more and less functional. That information will not be added to the metadata of the images yet as this happens during the face recognition workflow, what is explained further down. These areas are written as digikam internal information in digiKams core database. These algorithms generate region where a face can be found, typically a rectangle. Most of them are OpenCV based, and work mostly fine in the background (excepted some technical issues with OpenGL cards acceleration used by OpenCV which introduce instability, but it's another challenge).

It is a group of algorithms to analyse the content of images, identify the distinctive regions such as eyes, nose, mouth, etc. The overall face detection, recognition and management workflowīefore this article goes into the details, an overall description of all involved parts is given in corresponding order. If you read the post, you will notice that it content goes beyond the pure face management workflow. The post what made this change was written on the 01.Feb.2019 and describes quite well what has to be polished and redesigned, respectively. Eventually, it found its course in early 2019 what convinced the maintainer of digiKam to refurbish these features earlier than originally considered. We begin with a little story, explaining how all the digiKam face recognition related features became a GSoC project.Īll began in early 2018 as the thread either face recognition screen is buggy or I still don't understand it - at least I can say that more convenient bulk change of face tags (no auto refresh/set faces via context menu) is neccessary took off. 2 the overall face detection, recognition and management workflow.

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