Face recognition

Enhance your model with scalable data and granular control

Fast and Hyperrealistic
100k+ Identities
with broad diversity
Intra-class
variance per ID
Full privacy compliance
Segmentation
map
Rich Meta
data

Solve your data challenges

Face recognition models must be trained on a wide range of human faces in various conditions such as lighting, pose, accessories, expressions, camera positions and more. As well, obtaining real-world data for face recognition tasks is often complicated by a lack of diversity and complex privacy regulations.   Our synthetic data solution solves these challenges with the following attributes:

Fast and Hyperrealistic

Generate up to 4K high-quality images with outstanding detail and resolution

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100k+ identities with broad diversity

Use our API to control for ethnicity, age, gender, etc.

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Intra-class variance per ID

Our API also enables granular control of expression, head pose, eye gaze, lighting, glasses, hairstyles, etc.

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Full privacy compliance

Eliminate privacy concerns with our PII-free synthetic data, including for teenage identities.

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Segmentation map

Get accurate annotations including never possible before semantic segmentation

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Rich Metadata

Receive images accompanied by all needed metadata such as demographic, pose, light conditions, etc.

metadata
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Code template

Our code template can get you started with generating data for training a face recognition model. Use our template and SDK to code in Python for over 100K+ identities including age, gender, ethnicity and more.

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Industries working with face recognition

Security
Cosmetics
Facial analysis applications
In-cabin automotive
Fitness
Smart office

Related resources

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