Implement emotion recognition with static, dynamic, and video-based models

[Copilot]

Emotion recognition system comparing three temporal modeling approaches: static frame extraction, dynamic images via rank pooling, and LSTM-based video classification. Supports 7 emotion classes with face-focused detection.

Implementation

Three approaches, 9 model configurations:

• Static: Single frame + CNN (ResNet18/50, MobileNet V2)
• Dynamic: Rank pooling temporal aggregation + CNN
• Video: 16-frame sequences + CNN features + LSTM

Data pipeline:

• MTCNN face detection with Haar Cascade fallback
• Automatic face cropping and normalization
• Handles variable-length videos (3-16s)

Training infrastructure:

• Early stopping, LR scheduling, GPU memory tracking
• Per-epoch timing and resource monitoring
• Automated training loop for all model combinations

Metrics & Analysis

Comprehensive evaluation:

• Top-1 accuracy, Macro-F1, per-class ROC-AUC
• Confusion matrices, classification reports
• Error buckets by class and confusion pairs

XAI:

• Grad-CAM visualization with dual overlays
• Dynamic image temporal aggregation visualization
• Model attention region analysis

Outputs:

• CSV comparison table across all 9 models
• Training curves (loss, accuracy, time, memory)
• Per-class ROC curves
• JSON results for programmatic access

Deliverables

Notebook: Emotion_Recognition_Model.ipynb (24 cells, automated training pipeline)

Utilities:

• setup_dataset.py - Creates required directory structure
• verify_dataset.py - Validates dataset before training
• inference_example.py - Model usage demo

Documentation: Setup guide, technical summary, usage instructions (~30KB)

Usage

# Dataset structure
train/{neutral,surprise,sad,happy,fear,disgust,angry}/*.mp4
test/{neutral,surprise,sad,happy,fear,disgust,angry}/*.mp4

# Run notebook - trains all 9 models, generates full analysis
jupyter notebook Emotion_Recognition_Model.ipynb

Example dynamic image generation:

frames = extract_frames(video_path, max_frames=16)
faces = [detect_and_crop_face(f) for f in frames]
dynamic_img = generate_dynamic_image(faces, method='rank_pooling')

Model comparison output:

Mode	Architecture	Accuracy	Macro-F1	neutral_AUC	surprise_AUC	...
Static	ResNet18	0.xxxx	0.xxxx	0.xxxx	0.xxxx	...
Dynamic	ResNet50	0.xxxx	0.xxxx	0.xxxx	0.xxxx	...
Video	MobileNet	0.xxxx	0.xxxx	0.xxxx	0.xxxx	...

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