ID Verification
Synthetic data for computer vision to enable more capable and ethical AI.
Financial, government, healthcare, and other organizations are increasingly using facial recognition for identity verification.
Applications such as Face ID for iOS, Face Authentication for Android, AML/KYC systems, payment gateways, contactless ticketing, and physical access controls rely on accurate biometric authentication. However, the industry faces challenges with AI bias, privacy regulations, and fraud. Recent events have highlighted the strong bias in existing datasets, and most RFPs for biometric vendor technologies now include requirements for managing demographic bias.
The development of more performant models has been hampered by the inability to capture human data due to privacy concerns and regulations. As the use of biometric authentication has increased, so has the rate of fraud and spoofing. The industry is struggling to improve algorithms that reduce harm from malicious actors. To address these issues, Synthesis AI customers are increasingly turning to synthetic data to build more inclusive and privacy-compliant biometric models.
Diverse Faces
Bias in AI models is a result of non-representative and unbalanced training data. Our synthetic data generation platform contains 100,000 unique identities available spanning sex, age, skin tone, and body type to help build more inclusive models.
Variable Appearances & Accessories
Robust model performance is reliant on capturing variable training data for an individual. With complete control of pose, hair, facial hair, masks, glasses, and more, our synthetic data platform is able to create limitless inter- and intra-subject variability.
Lighting & Environment
Capture individuals across a wide-range of indoor and outdoor environments and lighting conditions. Easily capture data across harsh or low light conditions to ensure robust performance.
Camera Angle and Pose
Each product and camera system is distinct, and our platform enables the generation of representative data across imaging modalities and lens types, and with any camera position.
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