Tis article describes the current state of the face detection algorithms of digiKam and the desired outcome of the corresponding GSoC project.
It is recommended to read Faces Management workflow improvements, as this describes the entire face management workflow. Thus it helps to understand the scope of these algorithms and where it need clarification about its structure and interfaces with other parties (code modules).
Currently, there are four different methods using the corresponding algorithm, which are more or less functional. The used algorhtmy can be chosen in the one Face Scan dialog.
The goal is to be able to recognize automatically faces in images, which are not tagged, using a previous face tag registered in the face recognition database. The algorithms are complex but explained in more detail below.
=currently implemented face recognition algorythms
- Deep Neural Network (DNN) DLib
This is an experimental implementation of neural network to perform faces recognition.
This DNN is based on DLib code, a low level library used by OpenFace project. This code works, but it slow and complex to maintain. It is rather a proof of concept than being used for productive use.
Moreover the documentation in the source code is non-existant.
- OpenCV - lbph
This is the most complete implementation of a face detection algorythm. Moreover it is the oldest implementation of such an algorythm in digiKam. It's not perfect and require at least 6 face already tagged manually by the user to identify the same faces in non-tagged images.
This algorithm records a histogram of the face in the database, which is used later to perform the comparisons against new/non-tagged faces. This one use OpenCV backend.https://towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b
- OpenCV - Eigen Faces
An alternative algorithm what uses the OpenCV backend. It was introduced to have different source of results for face detection, enabling to proof the DNN approaches.
- OpenCV - Fisher Face
Another algorithm what uses the OpenCV backend. It was introduced for the same pruposes as Eigen Faces.
According rumors this one is not finalized, it is said that not all methods are implemented.
Theree is paper explainingl the difference between Fisher and Eigen Faces, see http://disp.ee.ntu.edu.tw/~pujols/Eigenfaces%20and%20Fisherfaces.pdf
why so many differnt approaches?
The idea why four different algorythm were implmented is simply to be able make an comprehnsive assesment of the currntly avaialbe tehcnolgoeies applicalbe ni digiKam and eventuelly choose the best one.
The student who worked on the DNN project few years ago has concluded that DNN was the best method to recognize with less error as possible. Unfortuentley the training and recognition process took too longs and slowed down the application. Regardless that fallback, it is agreed that DNN is the best way to go, but not using the current implementation based on DLib.
All code code was introduced by a student Yingjie Liu <[email protected]> in a previous GoSC project
All the low level steps to train and recognize faces are done in this class. For the middle level codes, muti-threaded and chained, started by the face scan dialog, all is here: https://cgit.kde.org/digikam.git/tree/core/utilities/facemanagement?h=development/dplugins
Why 4 kind of recognition algorithms
With 3.x versions, OpenCV has introduced a DNN API. I shall be used instead of the others approaches as done for the face detection
6/ Which kind of info are stored in database? This depend of recognition algorithm used. Histograms, Vector, Binary data, all responsible of algorithm computation, and of course all not compatible. Typically, when you change the recognition algorithm in Face Scan dialog, the database must be clear as well. But in fact this kind of database mechanism must be dropped, when DNN algorithm will be finalized, and only this one retained to do the job. As i said previously, 4 algorithms are implemented to choose the best one. At end, only one must still in digiKam face engine, and all the code must be simplified.