Didit Docs

Liveness Detection

Didit's Liveness Detection solution provides enterprise-grade biometric verification through advanced computer vision and machine learning algorithms. Our system achieves 99.9% accuracy with a false acceptance rate (FAR) of less than 0.1%, ensuring robust protection against spoofing attacks.

Liveness Detection Methods

Our platform implements three distinct anti-spoofing technologies, each tailored to different security needs and user experiences:

Method	Description	Security Level	Best For
`3D Action & Flash`	• Combines multi-factor biometric verification with a randomized action sequence and dynamic light pattern analysis. • At the start, the user is prompted to perform a simple action—like blinking or nodding—ensuring real-time interaction. • Simultaneously, the system projects a sequence of light patterns onto the face, analyzing the reflections to confirm the face's three-dimensional structure. • Deep learning algorithms examine micro-expressions and the light reflection responses to verify the presence of a live person. • Offers the highest security by integrating behavioral (action) and physical (light-based depth) cues, making it nearly impossible to spoof with static images, videos, or even advanced masks.	Highest	Banking, healthcare, government applications
`3D Flash`	• Uses dynamic light pattern analysis to validate facial topology without requiring user interaction. • Projects a series of light patterns onto the face at over 30 frames per second, analyzing the reflections to create a depth map. • This depth map confirms the face's three-dimensional structure, distinguishing it from flat images or 2D spoofs. • Provides a seamless experience while maintaining high security against presentation attacks like photos or screens.	High	Financial services, account access, identity verification
`Passive Liveness`	• Relies on single-frame deep learning analysis to detect signs of liveness. • Examines the image for artifacts, texture patterns, and other subtle indicators that differentiate a real face from a spoof. • A convolutional neural network (CNN) validates facial features and identifies anomalies, such as those from printed photos or digital screens. • Offers fast and convenient verification but provides standard security, suitable for low-risk use cases.	Standard	Low-friction scenarios, consumer applications

Advanced Security of 3D Flash and 3D Action & Flash:

These methods are engineered to defeat sophisticated spoofing attacks, such as high-quality masks, deepfakes, and video replays.
By projecting dynamic light patterns and analyzing their reflections, they detect how light interacts with a real 3D face versus a flat or artificial surface.
The 3D Action & Flash method adds an extra layer of security with a randomized action (e.g., blink or nod), requiring real-time behavioral responses that pre-recorded media or synthetic identities cannot replicate.
Proven to deliver high accuracy and low false acceptance rates, these methods are ideal for high-stakes environments.

Each method generates a normalized liveness score (0-100%) based on our proprietary algorithm, which evaluates multiple security factors in real time.

Configurable Thresholds

You can customize security levels by setting different thresholds for liveness scores. For example:

These thresholds can be adjusted based on your risk tolerance and security requirements, offering flexibility across use cases.

How It Works

Video Selfie Capture

Users are guided through a simple, intuitive interface tailored to the selected liveness method.
Real-time feedback ensures proper lighting, positioning, and framing.
Quality checks monitor for blur, glare, and optimal facial visibility.
Adaptive capture adjusts to various device capabilities and network conditions.

Liveness Detection Analysis

Advanced algorithms process the captured media in real time to detect spoofing attempts.
For 3D Action & Flash:
- Verifies the user's action (e.g., blink or nod) to confirm it's performed correctly and live.
- Analyzes light pattern reflections to validate the face's three-dimensional structure.
- Uses deep learning to assess micro-expressions and other cues for enhanced spoof detection.
For 3D Flash:
- Processes a sequence of light pattern reflections to build a detailed depth map.
- Confirms the face's 3D properties, distinguishing it from flat or 2D spoofs.
For Passive Liveness:
- Applies deep learning to a single frame to detect texture patterns and artifacts.
- Uses a CNN to validate facial features and identify potential spoofs.
Multi-layered detection identifies presentation attacks (e.g., photos, screens, masks).

Result Processing & Verification

The system compares the liveness score against your configured thresholds.
An automated decision engine applies your business rules for approval or rejection.
Suspicious cases can be flagged for manual review in your dashboard.
Results are delivered via API, webhooks, or admin portal, with detailed audit logs for compliance.

Warnings Report