Serious and complex memory leaks in the face management pipeline. The performance is better than with previous versions, as the implementation supports multiple cores to speed up computations. With this feature with your own collection. Recognition also includes a Sensitivity/Specificity settings to tune the results' accuracy, but we advise you leave the default settings as you begin experimenting Recognition rate with deep learning is really excellent and increases to 95%, where older algorithms were not able to reach 75% in the best of cases. Recognition can start to work with just one face tagged, where at least 6 items were necessary to obtain results with the previous algorithms.īut of course, if more than one face is already tagged, recognition is more likely to return good results. See the screenshot below taken while running the face recognition process. If new items are recognized, the automatic workflow will highlight new faces with a green border around a thumbnail and will report how many new The neural network will parse the faces already detected as unknown and compare them to ones already tagged. The user must tag some images with the same You need to teach the neural network with some faces so that it automatically recognizes them in a collection. The recognition workflow is still the same as in previous versions but it includes quite a few improvements. See examples below of face detection challenges performed by the neural network. The results processed over huge collections give excellent results The neural network model that we use is really a good one as it can detect blurred faces,Ĭovered faces, profiles of faces, printed faces, faces turned away, partial faces, etc. Run-time speed, and improved the success rate which reaches 97% of true positives.Īnother advantage is that it is able to detect non-human faces, such as those of dogs, as you can see in this screenshot.īut there are more improvements to face detection. No learning stage is required to perform face detection and recognition. The new code, based on recent Deep Neural Networkįeatures from the OpenCV library, uses neuronal networks with pre-learned data models dedicated The goal of this project was to leave behind all the old ideas and port the detection and the recognition engines to more modernĭeep-learning approaches.
#FASTRAWVIEWER ICONS CODE#
We tried again this year, and a complete rewrite of the code was successfully completed by a new student named Learning option in Face Management was never activated for users. The approach to resolve the problem took a wrong turn and that is why the deep Technical proof of concept, but not usable in production. The result was mostly demonstrative and very experimental, with poor computation speed results. Who worked on the integration of Neural Networks into the Face Management pipeline based on the Dlib library. Also, according to user feedback fromīugzilla, Face Recognition does not provides a good experience when it comes to an auto-tag mechanism for people.ĭuring the summer of 2017 we mentored a student, Yingjie Liu,
Of user feedback to confirm whether or not what it has detected is really a face. This works, but does not provide a high level of positive results.įace detection is able to give 80% of good results, while analysis is not too bad but requires a lot Used the classical feature-based Cascade Classifierįrom the OpenCV library.
Until now, the complex methodologies that analyzed image contents to isolate and tag people’s faces It had the problem of not being powerful enough to facilitate the faces-management workflow automatically. That included this feature (digiKam 2.0.0). The algorithms used in the background (not based on deep learning) were old and had been unchanged since the first revision Deep-Learning Powered Faces Managementįor many years, digiKam has provided an important feature dedicated to detecting and recognizing faces in photos. This version is a result of a long development that started one year ago and in which we have introduced new features and plenty of fixes.Ĭheck out some of the highlights listed below and discover all the changes in detail. Just in time to get you into the holiday spirit, we are now proud to release digiKam 7.0.0 final release today.
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