Paravision offers AI‑driven biometric APIs that deliver face‑recognition, liveness detection, deepfake detection, and age‑estimation with sub‑0.1 % false‑accept rates across diverse demographics, lighting conditions, angles, and masked faces. The modules run on cloud, edge, or mobile runtimes with low‑latency inference and GDPR‑compliant encryption, serving identity verification platforms, financial services, travel security, and retail payment processors.
Funding
$23M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JVFounders
Product
Problem
Organizations that require reliable identity verification face increasing fraud from synthetic media, spoofed biometric captures, and demographic bias in traditional facial recognition systems. Inconsistent performance across lighting conditions, angles, and mask usage further limits deployment in high‑throughput environments such as airports, retail points of sale, and digital KYC workflows. These gaps create security vulnerabilities and friction for both providers and end‑users.
Solution
Paravision delivers a suite of AI‑driven biometric modules that can be embedded via RESTful APIs into any identity‑centric application. The face‑recognition engine is calibrated to maintain sub‑0.1 % false‑accept rates across diverse demographics, off‑angle poses, and masked faces, meeting NIST and DHS benchmark standards. Liveness detection validates a live user from a single selfie by analyzing micro‑motion and texture cues, eliminating the need for specialized hardware. Deepfake detection scans video and image streams for synthetic artifacts, providing an additional layer of fraud defense. An optional age‑estimation model supports regulatory age‑verification use cases. All components are optimized for deployment on cloud, edge, or mobile runtimes, enabling low‑latency inference in constrained environments.
Features
- Ultra‑accurate face‑recognition model with demographic parity and robust performance under poor lighting, extreme angles, and facial coverings
- Real‑time liveness detection that distinguishes live subjects from photo, video, or mask attacks using a single selfie capture
- Deepfake detection engine leveraging convolutional‑transformer hybrids to flag synthetic media in both image and video streams
- Age‑estimation algorithm with ±1‑year accuracy for compliance‑driven age‑verification scenarios
- Scalable SDK and API suite supporting cloud, edge, and on‑device deployments with GPU/CPU acceleration options
- Continuous benchmarking against NIST/DHS datasets to ensure state‑of‑the‑art error rates
- End‑to‑end encryption and GDPR‑compliant data handling throughout the processing pipeline