
Identification of speakers
How can I recognize new speakers in my media assets in the future?
use case: Generate datasets automatically
the challenge
Although training AI-Models is already easy, there are so many unknown people in many archives that even uploading three images of the respective person leads to a massive time investment. Thousands of hours of video footage thus turn into a data silo with no use for the information it already contains. Is there a way to use DeepVA to automatically create training data from specific individuals?
the solution
Thanks to DeepVA’s Deep Collector, training data for face recognition can be obtained automatically. The AI of the Deep Collector recognizes lower-third titles, it checks the quality of the face present in the image and, if the quality is good, stores individual frames as training data together with the name extracted from the lower-third. The system can be used on-premise or in the cloud. If it is to be part of a workflow, integration is required. Via our RESTful API, it can be easily integrated in any existing system or workflow. Data protection requirements usually play a major role in this decision and should be considered.
What results can be obtained?
Automated creation of a training database out of available video data
Constant expansion of dataset through permanent analysis and training
Integrated Quality Checks for best possible training data
faster data acquisition
COST REDUCTION
faster labelling
Automatically build a face recognition dataset
Using images of the faces and text information (e.g. from belly bands), Face Dataset Creation automatically creates entire datasets, without any manual labor.
Contact us
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Related use cases
How can I recognize new speakers in my media assets in the future?
How can I automatically build training datasets from my media footage?
How can I identify and label people without pre-trained face recognition?
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
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