Face recognition works by first detecting faces in an image, then extracting key facial features (like distance between eyes, nose shape, jawline) to create a unique digital template. This template is compared against a database of known faces (1:N) or matched against a single provided image (1:1) for verification—all in real‑time.

Modern AI‑based face recognition systems achieve accuracy rates of 99.5‑99.9% under controlled conditions. Our algorithms are trained on diverse datasets to handle variations in lighting, pose, age, and accessories, delivering reliable performance even in challenging real‑world scenarios.

Liveness detection is an anti‑spoofing mechanism that verifies the user is a real, live person—not a photo, video, or 3D mask. It analyzes subtle cues like eye blinking, head movement, skin texture, and depth to prevent presentation attacks and ensure biometric authenticity.

Yes. Our face recognition supports mask‑aware detection and can identify individuals using the upper facial region (eyes, eyebrows, forehead). It can also detect if a mask is worn, enabling compliance monitoring in healthcare, retail, and public settings.

1:1 (one‑to‑one) compares a live face against a single enrolled image for verification—e.g., unlocking a phone or verifying an identity document. 1:N (one‑to‑many) compares a face against an entire database to identify a person—e.g., finding a suspect in a crowd or checking for watchlist matches.

Yes. Our solutions include multi‑level liveness detection (3D depth, infrared, and motion analysis) to defend against photos, videos, and 3D masks. Additionally, all biometric data is encrypted and processed securely, with options for on‑device processing to minimize privacy risks.

Key applications include: access control for buildings and rooms, time & attendance, mobile authentication, identity verification for KYC, watchlist monitoring for security, crowd analytics, personalized retail experiences, and passenger processing at airports and borders.

Our AI models are trained on globally diverse datasets representing all ethnicities, age groups, and genders. This ensures fair and accurate recognition across populations. We continuously audit and update our models to eliminate bias and maintain high performance for all users.

Any standard IP or USB camera with at least 2MP resolution can work, but we recommend cameras with IR or depth sensors for better low‑light performance and liveness detection. For enterprise deployments, we provide optimized hardware bundles including cameras, edge boxes, and servers.

Cost depends on the number of cameras/endpoints, deployment model (edge vs cloud), database size, and features required. We offer flexible pricing from per‑seat monthly subscriptions to perpetual enterprise licenses. Contact us for a customized quote.

Yes. Our solutions are built with privacy‑by‑design, supporting data anonymization, user consent management, and data retention policies. We comply with GDPR, CCPA, and other regional regulations. On‑device processing ensures biometric templates can be stored locally without sending sensitive data to the cloud.

Absolutely. Our platform provides RESTful APIs, SDKs, and support for industry protocols (Wiegand, RS‑485, ONVIF) to seamlessly integrate with door controllers, intercoms, VMS platforms, and HR systems. We work with leading brands like HID, Paxton, and ZKTeco.

Our optimized AI engines deliver recognition in under 300ms for 1:1 verification and under 500ms for 1:N identification across databases of up to 50,000 faces. Edge deployments achieve even lower latency, making them ideal for high‑throughput access points.

Implementation involves: 1) Requirement analysis and site survey. 2) Hardware selection and installation (cameras, edge devices). 3) Enrolment of users (face capture). 4) Integration with existing systems. 5) Testing and staff training. Supali Infotech offers turnkey deployment with end‑to‑end support.