Video Emotion Recognition Project

A complete system to perform emotion recognition from video using deep learning and cloud infrastructure (AWS SageMaker, S3, etc.). This project demonstrates an end‑to‑end pipeline: data ingestion → preprocessing → model training → deployment / inference → visualization / analysis.

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Table of Contents

1. Overview
2. Architecture & Design
3. Core AI / ML Concepts
4. Cloud & Infrastructure
5. Setup & Usage
6. File Structure
7. Technologies Used
8. Evaluation & Metrics
9. Limitations & Future Work
10. Acknowledgements & References

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1. Overview

The goal of this project is to detect and classify emotional states (e.g. “happy”, “sad”, “angry”, “neutral”, etc.) from videos. Rather than static images only, the system handles temporal sequences (frames) and optionally leverages multimodal cues.

Key goals:

• Process video input (frame extraction, face detection, alignment)
• Extract visual features (via CNN / transfer learning)
• Model temporal dynamics (via LSTM, 3D CNN, or temporal fusion)
• Train, validate, and version models
• Deploy / serve the model in a scalable, cloud‑based environment
• Store data (raw videos, intermediate frames, logs) reliably
• Monitor and analyze model performance

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2. Architecture & Design

High‑level pipeline:

Video Input → Frame Extraction → Face Detection / Crop →  
Preprocessing / Augmentation → Feature Extraction (CNN) →  
Temporal Module (LSTM / 3D conv / attention) → Output Emotions

Example research diagrams for multimodal emotion systems:

Infrastructure mapping:

• Client / UI / Ingest → video upload
• Storage (S3) → raw video, frames, logs
• Compute (SageMaker, EC2) → training & inference
• Model repository / serving → hosted endpoint
• Monitoring (CloudWatch) → metrics, logs

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3. Core AI / ML Concepts

CNN / Transfer Learning

Use pretrained CNNs (e.g. ResNet, EfficientNet) to extract features and fine‑tune for emotion classification.

Temporal Modeling

• LSTM / GRU to model time dependencies
• 3D CNN for spatio‑temporal features
• Temporal attention layers for context awareness

Loss & Training

• Categorical cross‑entropy
• Class weighting for imbalance
• Data augmentation (flip, crop, brightness)

Evaluation Metrics

• Accuracy, Precision, Recall, F1‑score
• Confusion Matrix
• ROC / AUC curves

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4. Cloud & Infrastructure (AWS)

Amazon S3

Used for storing raw videos, extracted frames, processed datasets, and model artifacts.

s3://video-emotion-project/
   raw-videos/
   frames/
   processed-data/
   model-artifacts/
   logs/

AWS SageMaker

Handles model training and deployment.

• Training jobs (distributed, GPU‑accelerated)
• Hyperparameter tuning
• Real‑time endpoints
• Batch transform jobs
• Experiment tracking & versioning

IAM & Security

• Proper roles for SageMaker to access S3
• Encrypted storage and secure endpoints

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5. Setup & Usage

Prerequisites

• AWS account (S3, SageMaker access)
• Python 3.8+
• awscli configured

Clone Repository

git clone https://github.com/danielgavrila2/Video-Emotion-Project.git
cd Video-Emotion-Project

Install Dependencies

pip install -r requirements.txt

Local Training

python train_local.py --data-dir ./data --epochs 20 --batch-size 32

AWS SageMaker Training

python train_sagemaker.py   --s3-bucket my-bucket   --role-arn arn:aws:iam::123456789012:role/SageMakerRole   --region us-east-1   --epochs 30   --batch-size 32

Deploy Model Endpoint

from sagemaker import Model

model = Model(
    model_data='s3://my-bucket/model-artifacts/model.tar.gz',
    role='arn:aws:iam::123456789012:role/SageMakerRole',
    image_uri='your-container-uri'
)

predictor = model.deploy(initial_instance_count=1, instance_type='ml.m5.large')

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6. File Structure

Video-Emotion-Project/
├── training/
│   ├── model.py
│   ├── dataset.py
│   ├── train.py
│   └── utils.py
├── deployment/
│   ├── inference.py
│   └── serve_script.py
├── client/
│   ├── client.py
│   └── video_utils.py
├── train_sagemaker.py
├── batch_inference.py
├── requirements.txt
└── README.md

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7. Technologies Used

Layer	Technology	Purpose
DL Framework	PyTorch / TensorFlow	Model definition, training
Vision	OpenCV, Dlib	Frame extraction, face detection
AWS SDK	boto3, sagemaker	Cloud integration
Cloud	S3, SageMaker, IAM	Storage, compute, security
Deployment	Docker, AWS ECR	Containerization
Data	NumPy, Pandas	Processing
Logging	TensorBoard, CloudWatch	Metrics tracking

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8. Evaluation & Metrics

Include confusion matrix, precision/recall/F1 plots, and learning curves.
Sample evaluation:

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9. Limitations & Future Work

• Dataset imbalance
• Mixed emotions
• Lighting / occlusion issues
• Generalization across demographics
• Real-time deployment optimization
• Add multimodal (audio/text) data

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10. Acknowledgements & References

• FER‑2013, AFEW datasets
• AWS SageMaker documentation
• PyTorch & TensorFlow communities

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Author: Daniel Gavrila
License: MIT