Deepva face evaluation api
Face evaluation
Function description
Quality evaluation of user data
Building your own AI models for face recognition with DeepVA is an intuitive process and does not require any machine learning expertise. However, it is possible that the data provided by the user may limit the recognition performance of the AI model because it does not meet certain quality standards in terms of resolution, relative size or position. Face Evaluation analyses entire data sets and provides feedback to the user about the quality of their training data.
benefits
Transforming data into value
Individual person identification
Feedback on data quality without AI expertise
Anyone can build their own AI models
use cases
Face evaluation use cases
Face evaluation checks the quality of the training data. This means that all training data is checked for usability in a separate AI model: For example, if people are wearing masks or the images are too blurry, this data is recognised and cleaned up. This improves the quality of the dataset and makes face recognition more accurate and reliable.
Individual person identification
How can I recognize less-known or regional people in my footage?
