Liveness Detection-SenseFoundry Algorithm Marketplace

Liveness Detection In identity verification scenarios, it determines the real physiological characteristics of the target. For face recognition applications, it effectively resists attacks such as photos, masks, head models, and screen replays, identifies fraudulent behavior, and protects user interests.

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Algorithm Introduction

Application Value

FAQ

Algorithm recommendation

Algorithm Introduction

Silent liveness detection technology represents the current mainstream approach in liveness verification. The term 'silent' refers to the user being passively verified as a live person without requiring any active cooperation - such as performing specific actions or reading text. This technology employs deep learning algorithms that process a single image (captured by a single camera), typically either an RGB or NIR image, to output an attack probability score, ultimately determining whether the subject is a real person or a spoofing attempt (labeled as 'live' or 'fake').

● Modality: RGB
● Lighting Conditions: Primarily designed for normal indoor/outdoor lighting, strong light, low light, and backlit scenarios. Accuracy cannot be guaranteed under overexposed conditions (average pixel value >200 in facial regions) or extremely dark conditions (average pixel value

Application Value

Face Recognition Systems

Safeguards against spoofing attacks such as photo reproduction, video playback, and 3D masks to ensure secure identity verification.
Financial Payments

Enables identity authentication for bank account opening and mobile payment transactions.
Access Control & Security

Verifies that individuals passing through are physically present, enhancing security in restricted areas.
Government & Healthcare Services

Facilitates remote real-name authentication for government services and medical insurance identity verification.

FAQ

Algorithm Accuracy

All algorithms published on the website claim accuracies above 90 %. However, real-world performance drops can occur for the following reasons:
(1) Poor imaging quality, such as
• Strong light, backlight, nighttime, rain, snow, or fog degrading image quality
• Low resolution, motion blur, lens contamination, compression artifacts, or sensor noise
• Targets being partially or fully occluded (common in object detection, tracking, and pose estimation)
(2) The website provides two broad classes of algorithms: general-purpose and long-tail (rare scenes, uncommon object categories, or insufficient training data). Long-tail algorithms typically exhibit weaker generalization.
(3) Accuracy is not guaranteed in boundary or extreme scenarios.
Deployment & Inference

We offer multiple deployment formats—Models, Applets and SDKs.
Compatibility has been verified with more than ten domestic chip vendors, including Huawei Ascend, Iluvatar, and Denglin, ensuring full support for China-made CPUs, GPUs, and NPUs to meet high-grade IT innovation requirements.
For each hardware configuration, we select and deploy a high-accuracy model whose parameter count is optimally matched to the available compute power.
How to Customize an Algorithm

All algorithms showcased on the website come with ready-to-use models and corresponding application examples. If you need further optimization or customization, choose one of the following paths:
(1) Standard Customization (highest accuracy, longer lead time)
Requirements discussion → collect valid data (≥1 000 images or ≥100 video clips from your scenario) → custom algorithm development & deployment → acceptance testing
(2) Rapid Implementation (Monolith：https://monolith.sensefoundry.cn/)
Monolith provides an intuitive, web-based interface that requires no deep AI expertise. In as little as 30 minutes you can upload data, leverage smart annotation, train, and deploy a high-performance vision model end-to-end—dramatically shortening the algorithm production cycle.