Age Estimation Methods
Our platform implements age estimation in conjunction with different liveness verification technologies:| 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. • For privacy, the user’s face appears blurry in the interface, assuring them that their image is being analyzed for age estimation only, not for identification. • 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 |
Configurable Thresholds
You can customize security levels by setting different thresholds for age estimation. For example:
These thresholds can be adjusted based on your risk tolerance and security requirements.
Per-Country Age Restrictions
Age restrictions can be configured on a per-country basis, reflecting the fact that the legal age of majority varies across jurisdictions. Instead of setting a single global minimum or maximum age, you can define specific age limits for each country — and even for individual states or regions within a country.How It Works
- Country-level configuration: Set a minimum and/or maximum age for each country (identified by the document’s issuing state). For example, you might require a minimum age of 18 in the United States, 19 in South Korea, and 21 in the United Arab Emirates.
- State/Region overrides: For countries with sub-national variation (such as the United States or Mexico), you can configure overrides per state. For example, Mississippi may require a minimum age of 21 while the US default is 18. The system matches the state using the region extracted from the document by OCR.
- Age of majority defaults: The console provides a one-click “Apply age of majority” button that auto-populates each country’s minimum age based on the known legal age of majority worldwide. You can then customize individual countries or states as needed.
- Configurable actions: When a user’s age falls below the minimum or exceeds the maximum for their document’s country, you can choose the action to take: Decline or Review.
Example Configuration
| Country | Min Age | Max Age | State Overrides |
|---|---|---|---|
| USA | 18 | — | Mississippi: 21, Alabama: 19 |
| KOR | 19 | — | — |
| GBR | 18 | 65 | — |
| ARE | 21 | — | — |
Per-country age restrictions are available in both standard KYC workflows and Adaptive Age Verification workflows. In adaptive workflows, when ID verification is triggered for borderline cases, the age check uses the per-country settings configured in the ID Verification step.
How It Works
Image or Video Capture
The user provides a clear facial image through API upload or completes a liveness verification process.
| Check | Description |
|---|---|
| Image quality | Validates lighting, positioning, and clarity |
| Liveness verification | Ensures the subject is present and not a spoof |
| Multi-frame analysis | Selects the optimal frame for best accuracy |
| Adaptive capture | Adjusts to various device capabilities |
Facial Feature Analysis
Advanced computer vision isolates the face and maps key facial landmarks.
- 80+ reference points mapped across the face
- Deep learning algorithms analyze facial morphology, proportions, and texture
- Demographic-specific features identified with precise pixel mapping
- Facial regions segmented for specialized analysis (eye region, jawline, skin texture)
Neural Network Processing
Convolutional neural networks (CNNs) process the extracted features through multiple layers.
- Model trained on millions of diverse faces across age ranges, ethnicities, and genders
- Feature vectors compared against age-correlated datasets with demographic calibration
- Multiple sub-models employed for different age brackets to enhance accuracy
- Cross-validation against complementary models for robust estimation
Decision & Supplementary Analysis
The system generates a comprehensive result:
| Output | Description |
|---|---|
| Age estimate | Primary estimate with confidence scoring |
| Gender estimation | Supplementary demographic context (optional) |
| Confidence metrics | Overall reliability assessment |
| Environmental factors | Impact assessment on estimation quality |
Rule Application
Your configured business rules are applied to the estimation results.
- Age estimate compared against your configured thresholds (min/max per country)
- Confidence scores checked against your minimum requirements
- Borderline cases can trigger ID verification fallback (Adaptive mode)
- Results documented with detailed audit trail for compliance
Adaptive Age Estimation
For scenarios where precise age verification is critical, our platform offers adaptive age estimation with ID verification fallback. This approach provides a balance between user convenience and regulatory compliance.How Adaptive Age Estimation Works
Initial Age Estimation
The system first attempts to estimate the user’s age using facial analysis. A confidence score is generated alongside the age estimate.
Threshold Evaluation
The estimated age is compared against your configured thresholds. Three outcomes:
| Outcome | Description |
|---|---|
| Clear Pass | User is clearly above your required age threshold |
| Clear Fail | User is clearly below your required age threshold |
| Borderline | Estimated age falls within an uncertain range or has low confidence |
Intelligent Fallback
- Clear pass/fail: Verification completes immediately with the appropriate result
- Borderline cases: System automatically initiates an ID verification flow
- This ensures regulatory compliance while minimizing friction for most users
Document Verification (when needed)
For borderline cases, the user provides a government-issued ID document:
- Document authenticity and age information are verified
- Per-country age restrictions from the ID Verification step are applied
- The system checks date of birth against the minimum/maximum age for the document’s issuing country and region
Adaptive age estimation can be configured using Adaptive Age Verification workflows type. You can define the borderline age thresholds that determine when ID verification is triggered. The final age-based approval or decline for borderline cases is then governed by the per-country age restrictions configured in the ID Verification step.
Benefits of Adaptive Age Estimation
- Reduced Friction: Most users complete verification with just a selfie
- Enhanced Compliance: Uncertain cases receive thorough document verification with country-specific age rules
- Cost Efficiency: ID verification is only used when necessary
- Customizable Risk Tolerance: Adjust thresholds based on your regulatory requirements
- Jurisdiction-Aware: Automatically applies the correct age of majority based on the user’s document issuing country and region
Model Performance and Statistics
Our age estimation technology is built on advanced deep learning models that deliver industry-leading accuracy. Below are key performance metrics based on extensive validation across diverse datasets.Accuracy Metrics
| Metric | Value | Description |
|---|---|---|
| Mean Absolute Error (MAE) | 3.5 years | Average difference between estimated and actual age across all age ranges |
| Standard Deviation | 1.2 years | Variation in estimation error across the dataset |
| Accuracy within ±5 years | 89% | Percentage of estimations within 5 years of actual age |
| Accuracy within ±3 years | 76% | Percentage of estimations within 3 years of actual age |
Performance Across Demographics
Our models are trained on diverse datasets to ensure consistent performance across different demographic groups.| Demographic Group | MAE (years) | Confidence Score |
|---|---|---|
| -18 age range | 1.5 | High |
| 18-25 age range | 2.8 | High |
| 26-40 age range | 3.2 | High |
| 41-60 age range | 3.9 | Medium-High |
| 60+ age range | 4.5 | Medium |
Our models are regularly retrained and validated to ensure consistent performance across changing visual conditions and demographic representation.