Age Estimation

Didit's Age Estimation technology provides enterprise-grade age verification through advanced facial analysis and machine learning. Our system delivers high accuracy with typical estimation within ±3.5 years for most age ranges.

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. • 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

Each method generates a precise age estimate along with confidence scores and supplementary gender estimation data.

Configurable Thresholds

You can customize security levels by setting different thresholds for age estimation. For example:

and also for liveness detection:

These thresholds can be adjusted based on your risk tolerance and security requirements.

How It Works

Image or Video Capture

User provides a clear facial image through API upload or completes a liveness verification process
System checks image quality, lighting, facial positioning, and clarity
For live estimation, the liveness verification ensures the subject is present and not a spoof
Multiple frames may be analyzed to select optimal image quality

Facial Feature Analysis

Advanced computer vision isolates the face and maps key facial landmarks (80+ reference points)
Deep learning algorithms analyze facial morphology, proportions, and texture patterns
Demographic-specific features are identified and measured with precise pixel mapping
Facial regions are segmented for specialized analysis (eye region, jawline, skin texture)

Neural Network Processing

Convolutional neural networks (CNNs) process extracted features through multiple layers
Model trained on millions of diverse faces across age ranges, ethnicities, and genders
Feature vectors are compared against age-correlated datasets with demographic calibration
Multiple sub-models may be employed for different age brackets to enhance accuracy

Decision and Supplementary Analysis

Primary age estimate is generated with confidence scoring
Gender estimation provides supplementary context (optional)
Environmental factors are assessed for potential impact on estimation quality
Comprehensive confidence metrics evaluate overall reliability of the estimation

Rule Application

System applies your configured age thresholds to the estimation results
Confidence scores are compared against your specified minimum requirements
Appropriate action is taken based on your defined business rules
Results are documented with detailed audit trail for compliance purposes

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 possible outcomes are determined:
- Clear Pass: User is clearly above your required age threshold
- Clear Fail: User is clearly below your required age threshold
- Borderline Case: User's estimated age falls within an uncertain range or has low confidence

Intelligent Fallback

For clear pass/fail cases, the verification completes with the appropriate result
For borderline cases, the system automatically initiates an ID verification flow
This fallback ensures regulatory compliance while minimizing friction for most users

Document Verification (when needed)

User is prompted to provide a government-issued ID document
Document authenticity and age information are verified
Final verification decision is based on the document's data

Adaptive age estimation can be configured using Adaptive Age Verification workflows type. You can define the age thresholds and confidence levels that trigger the ID verification fallback.

Benefits of Adaptive Age Estimation

Reduced Friction: Most users complete verification with just a selfie
Enhanced Compliance: Uncertain cases receive thorough document verification
Cost Efficiency: ID verification is only used when necessary
Customizable Risk Tolerance: Adjust thresholds based on your regulatory requirements

This approach is particularly valuable for age-gated services like online gaming, alcohol delivery, and adult content platforms where balancing user experience with regulatory compliance is essential.

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
18-25 age range	2.8	High
26-40 age range	3.2	High
41-60 age range	3.9	Medium-High
61+ age range	4.5	Medium

Our models are regularly retrained and validated to ensure consistent performance across changing visual conditions and demographic representation.

Warnings Report