A set of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age, and gender
The Face analyzer consists of several cutting-edge ML models. Its primary function is to detect visible human faces in images and predict some facial characteristics that are deemed important.
Leveraging state-of-the-art deep learning algorithms and neural networks, the Analyzer accurately identifies and analyzes faces, extracting the following information for each face:
position in the image (bounding box);
facial landmarks (coordinates of points that map to specific facial structures on the face);
expression classification (happy, angry, sad, etc.);
ethnicity classification;
age estimation;
gender classification.
Use cases for automatic facial analysis include:
Image tagging and organization - Automatic facial analysis enables users to easily categorize and index images based on expressions, ethnicities, age groups and genders. This streamlines content management, making it easier to locate specific images for various purposes.
Inclusive representation - The Face Analyzer can facilitate inclusive representation in media content. By analyzing facial ethnicities and genders, content creators can ensure diversity and cultural representation in their visual assets, thus promoting inclusivity.
Search Optimization - Automatic tagging and categorization of images based on facial characteristics allow users to find specific faces, expressions, or ethnicities with ease.
An up-to-date reference with all API endpoints is available here:
| Input image | Input image |
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| Output preview | Output preview |
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| API response | API response |
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