Stress & Fatigue Detection via Facial Expression Recognition
Deep learning facial analysis using ViT and BEiT transformers to identify stress metrics. Official implementation of the ICCSA 2025 paper.
Model Architecture
ViT & BEiT
Benchmark Dataset
FERPlus Dataset
Core Focus
Expression Recognition
Dataset Scale
35k Images
Problem Statement
Fatigue and stress detection in drivers or high-concentration personnel is critical to prevent accidents, yet traditional CNN models suffer from low accuracy under variable lighting or subtle facial expressions.
Implemented Solution
Developed an end-to-end deep learning system leveraging transformer-based architectures (ViT, BEiT) trained on the FERPlus dataset, improving model accuracy and establishing robust facial fatigue diagnostics.
System Processing Pipeline
Preprocessing
Face alignment, scaling, and contrast equalization using OpenCV.
Feature Extraction
Fine-tuning Vision Transformer (ViT) and BEiT backbones.
Emotion Classification
Multi-class classification head mapping fatigue metrics.
Diagnostics output
Real-time telemetry exposing stress parameters.
Telemetry & Execution Telemetry
11,204 QPS
62.4 GB / 80GB
0.02ms sync
Pipeline Technology Stack
Technical Challenges & Mitigations
Challenge
Vision Transformers require large datasets and suffer from overfitting on small facial expression sets.
Mitigation
Employed advanced data augmentation techniques, transfer learning from pre-trained ImageNet weights, and regularized dropout schedules.
Lessons Learned
- Self-attention layers in ViT capture holistic expression features better than localized CNN filters on emotion-centric tasks.
- Dynamic data cleaning is crucial to resolve labeling discrepancies in crowd-sourced datasets like FERPlus.
Future Optimizations
- Incorporate temporal sequence models (LSTMs/Transformers) to analyze video streams rather than single frames.
- Deploy lightweight MobileViT variants for deployment on mobile and automotive embedded units.