The Deep Research Analysis Engine+Merlin AI system implements a multi-level pipeline for facial analysis to predict personality traits and behavioral characteristics. The architecture consists of four main components.
Technology Stack and Tools
Face Detection
YOLOv5 - 99.97% accuracy on test set, 20ms on GPU
Facial Landmarks
Proprietary ensemble of 500 regression trees (5.5 normalized mean error vs 8.7 for IBUG 300-W)
Feature Extraction
Geometric calculations through Euclidean distances between 68 landmark points
Face Frontalization
StyleGAN2 encoder-decoder pipeline for pose normalization
ML Models: Ensemble
1000 trees, depth 6, L2 leaf regularization
1200 trees, max depth 7
1500 trees, num_leaves 31
System Performance
| Metric | Value |
|---|---|
| Processing time per photo | 0.1–0.3 sec |
| Total time with network latency | ≤1 sec |
| Throughput | ~3000+ photos/hour |
| Speed improvement | 13x acceleration (from 4 sec to 0.1–0.3 sec) |
| Final AUC accuracy | 0.75 ±0.01 |
| Face detection accuracy | 99% |
Facial Landmarks (Key Points on Face)
The system uses 29 critical landmark points on the face to calculate 19 facial features using neurotypology methodology.
Facial Features Used in Analysis
| № | Feature | № | Feature |
|---|---|---|---|
| 1 | Jaw asymmetry | 11 | Eye size |
| 2 | Eyebrow asymmetry | 12 | Eye spacing width |
| 3 | Eyebrow height | 13 | Ear protrusion |
| 4 | Eyebrow angle | 14 | Cheekbones |
| 5 | Eye slant | 15 | Jaw width |
| 6 | Mouth size | 16 | Head shape |
| 7 | Upper lip fullness | 17 | Upper eyelid exposure |
| 8 | Lower lip fullness | 18 | Mouth corner asymmetry |
| 9 | Eye asymmetry | 19 | Nose angle |
| 10 | Eye size asymmetry |
Photo Processing Pipeline
Example of Feature Calculation (Head Shape)
- Head height (H): distance between points 0 and 10 in pixels
- Head width (W): Euclidean distance between points 8 and 12
- Calculation: Shape = H / W
Interpretation: the higher the ratio, the more elongated the head.
Optimized Facial Landmark Detection Model
Original Model
- Dataset: IBUG 300-W (open dataset)
- NME: 8.7
- Problem: Low quality led to localization errors
Current Optimized Model
- Custom dataset: 2,700 manually annotated high-quality images
- Architecture: Ensemble of 500 regression trees, HOG descriptors, tree depth 4
- Training: 3-fold cross-validation
- NME: 5.5 (✓ 37% improvement)
- Speed: 3ms for 68-point localization on GPU
Data Normalization
All features are normalized using min-max scaler based on quantiles:
- Lower bound (0): 0.01 quantile of the sample
- Upper bound (1): 0.99 quantile of the sample
- Average value: 0.5
Approach advantage: Excludes the influence of anomalous values on the sample and ensures model robustness to outliers.
Problem
The feature extraction algorithm loses accuracy when the head deviates from frontal position by more than 5-15% along the yaw axis (horizontal rotation):
Critical Finding: At ≥15% deviation from ideal angle, the model shows AUC 0.56 (29% accuracy loss). This demonstrates critical importance of correct frontal angles for analysis.
Solution: Face Frontalization
StyleGAN2 architecture is used for automatic face pose alignment.
Frontalization Mechanism (StyleGAN2 + Pix2Style2Pixel)
- Backbone: ResNet-50 with custom fully connected layers
- Output: 512-dimensional face descriptor (w+ space StyleGAN2)
Step 1 - Encoding
StyleGAN2 encoder transforms the original photo into a face descriptor (512-dimensional vector). This vector contains complete representation of facial features.
Step 2 - Pose Estimation
3D face model fitting using landmark constraints. Output: Yaw, pitch, roll angles.
Step 3 - Latent Space Modification
Face descriptor is modified using pose rotation vectors:
where α and β are calculated based on estimated pose angles. Target state: ideal frontal position.
Step 4 - Decoding and Analysis
Modified descriptor is decoded back to pixel space. 19 facial features are measured on the aligned image. Features are now extracted from standardized pose.
Method Effectiveness
Processing range: angles with deviation up to 15% from frontal
Technology: Proprietary StyleGAN2 encoder-decoder pipeline
Processing time: 150ms on NVIDIA Tesla V100 GPU
Result: System becomes invariant to moderate head deviations
Clinical Research (EEG Study)
Experiment with 300+ volunteers:
- Participants observed hundreds of AI-generated facial images while brain activity was recorded (EEG)
- Focused on specific features (older-looking faces, smiles)
- EEG signals were analyzed by neural network to determine if brain recognized images matching the imagined features
- Neural network adjusted predictions based on EEG feedback
Brain Structure ↔ Facial Feature Correlations
Scientific findings (with peer-reviewed study references):
| Brain Structure | Facial Feature | Correlation | Source |
|---|---|---|---|
| Amygdala size | Aggression, instincts | Smaller size → lower aggression | Frontiers in Human Neuroscience (2017) |
| Parietal lobes | Associative thinking | More developed → higher logic | Nature Scientific Reports (2022) |
| Frontal lobes | Behavioral control | More pronounced → better social norms | Nature Scientific Reports (2024) |
| Visual cortex | Eye shape | Larger eyes → higher visual perception | Nature Scientific Reports (2020) |
| Somatosensory cortex | Lip fullness | Full lips → higher tactile sensitivity | Frontiers in Public Health (2022) |
Model Accuracy Metrics
| Model Configuration | AUC Score | Description |
|---|---|---|
| Survey data only | 0.70 | Baseline model (gender, age, education, profession) |
| Facial features only | 0.65 | Independent analysis of 19 features without survey data |
| Combined model | 0.72 | Facial features + survey data (basic combination) |
| Final optimized | 0.75 ±0.01 | With ensemble learning and hyperparameter optimization |
| Combined with behavior | 0.75 ±0.01 | + 237 behavioral data points |
Prediction Stability
- Variation range: 0.74–0.76 on different test sets
- Interpretation: ±0.01 deviation indicates properly trained model
- Dependency: Accuracy directly depends on input data quality (photo clarity, lighting, frontal angle)
Feature Extraction
Process:
- Initial set: 700+ derived features from facial analysis
- Additional features: user behavior on platform
- Selection method: Recursive feature elimination with cross-validation
- Final set: 50 most predictive features
Top-5 Features by Importance (SHAP values)
Ensemble Model Configuration
Gradient boosting models with weights:
- 1000 trees
- Depth 6
- Learning rate 0.03
- L2 regularization 3
- 1200 trees
- Max depth 7
- Learning rate 0.01
- Subsample 0.8
- 1500 trees
- Num_leaves 31
- Learning rate 0.05
- Feature_fraction 0.9
Calibration: Isotonic regression, Brier score after calibration:
Monitoring System
Retraining Schedule
- Full model retraining: Monthly
- Incremental updates: Weekly
Real-time Monitoring
- Daily metric calculation on new data
- Automatic alerts on increased negative interactions
- Quality and user satisfaction tracking
Actual Result: Detected gradual AUC decline of 0.02 over 6 months → model retraining initiated
A/B Testing
Experiment: Base model vs. version with additional behavioral features
Sample size: 10,000+ user interactions per variant
Result: New version showed AUC improvement of
Fairness Audit
Method: Equality of Opportunity
- Finding: False positive rate gap between income groups (0.07)
- Action: Implemented example weighting technique to reduce the gap
Deep Research Analysis Engine+Merlin AI Capabilities for Schools
Personality trait analysis (based on student photo):
Personality Type
- Socionics (16 types)
- MBTI (16 types)
Cognitive Abilities
- Physical/Informational Persistence
- Active/Passive Curiosity
- Intellectuality
Behavioral Predictors
- Aggression levels
- Ambition
- Foresight
- ADHD tendencies
Social Skills
- Behavior with others
- Demonstrativeness
- Emotionality
Validated Metrics for School Integration
| Metric | Value | Reliability |
|---|---|---|
| Personality classification accuracy | 83% | High (EEG validated) |
| AUC reliability | 0.75 ±0.01 | High |
| Analysis time | 0.3 sec | Production-ready |
| Scalability | 3000+ photos/hour | Enterprise-grade |
Ethical Recommendations for Schools
Consent
Granular opt-in for facial analysis
Transparency
Explanation of how features influence results
Anonymization
Irreversible conversion to 1024-dimensional vectors
Security
AES-256 encryption, TLS 1.3, Multi-factor authentication
Compliance
GDPR DPIA, CCPA, BIPA policies
1. Demographic Differences
Minor AUC variation across age groups (<0.03), slight decrease for minorities (AUC 0.05 lower)
2. Cultural Factors
Facial expression interpretation varies across cultures
3. Personality Dynamics
System assumes relative stability of traits
4. Angle Criticality
≥15% deviation reduces AUC to 0.56
Recommendation for Schools
Use Deep Research Analysis Engine+Merlin AI as a supplementary tool, not as the sole source of information about a student.
The Deep Research Analysis Engine+Merlin AI system represents a production-ready solution for analyzing personality characteristics based on photographs with scientific foundation (EEG studies, brain imaging correlations) and high accuracy (AUC 0.75, 83% consistency with EEG data). For schools, this means the ability to perform objective, fast, and scalable analysis of individual student characteristics for personalized learning.