Facial Expression Recognition with Vision Transformers

Have you ever wondered if a computer could read your emotions just by looking at your face? Facial expression recognition (FER) is a rapidly evolving field in computer vision that aims to do exactly that. This blog delves into the exciting world of FER, specifically focusing on a powerful new technique called Vision Transformers (ViTs).

The Power of Facial Expressions

Humans communicate a vast range of emotions through facial expressions. A raised eyebrow, a pursed lip, a wide smile – these subtle changes convey volumes of information. FER technology harnesses this power to create intelligent systems that can "understand" our emotions.

Applications Abound

The potential applications of FER are vast and ever-growing. Here are a few examples:

Human-Computer Interaction (HCI): Imagine systems that adjust to your mood, offering a more empathetic and personalized experience in applications, games, and virtual reality environments.
Affective Computing: Analyze user responses to content, leading to personalized recommendations in marketing or tailored educational approaches.
Surveillance and Security: Automated detection of suspicious or aggressive behavior in public spaces can enhance security measures.
Medical Diagnosis: Assist healthcare professionals in identifying emotional signs of depression, anxiety, or pain.
Education and Learning: Systems can monitor student engagement and emotional well-being, adapting instruction to cater to individual needs.

The Rise of Vision Transformers

Convolutional Neural Networks (CNNs) have dominated computer vision tasks for years. However, Vision Transformers (ViTs), introduced in 2020 by Dosovitskiy et al., offer a fresh perspective. Unlike CNNs, ViTs utilize an attention mechanism to process images:

Image Splitting: An input image is divided into smaller patches.
Patch Embedding: Each patch is converted into a vector representation using a linear transformation.
Positional Encoding: Relative positions of patches are encoded to capture spatial information within the image.
Transformer Encoder: A series of encoder layers process the embedded patches, learning relationships between them.
Classification: The final output is fed into a classifier head for emotion prediction.

Why ViTs for FER?

ViTs offer several key advantages over CNNs for FER tasks:

Global Context Awareness: ViTs excel at capturing long-range dependencies and global context within an image.This is crucial for FER, where subtle facial expressions involving multiple regions of the face hold the key to emotion classification.
Flexibility: ViTs can easily adapt to different input sizes and can be pre-trained on large image datasets for transfer learning to specific tasks like FER.

Let's Build Your Own FER System with ViT!

Ready to unleash the power of ViT for FER? Here's a detailed guide to get you started:

1. Setting Up the Project:

Clone the Repository: Use Git to clone the repository from GitHub:

git clone https://github.com/sairam-penjarla/facial-expression-recognition

Install Dependencies: Navigate to the project directory and install the required libraries using pip: Bash

pip install -r requirements.txt

Download Dataset:Important Note: This project does not include the dataset due to size and licensing restrictions. However, you can download the AffectNet training data from Kaggle: https://www.kaggle.com/datasets/noamsegal/affectnet-training-data
Once downloaded, extract the dataset files and place them in a designated folder within your project directory. The code will assume a specific directory structure for the dataset. You may need to adjust the paths in main.py if your structure differs.

2. Running the Script:

Navigate to the scripts directory within the project and execute the main script:

cd scripts 
python main.py

This script will:

Load the pre-trained ViT model
Load the downloaded facial expression dataset
Preprocess the images (resizing, normalization)
Train the ViT model on the dataset
Evaluate the model's performance
Predict facial expressions for new images (optional)

3. Exploring Further

This project provides a starting point for your FER exploration with ViTs. Here are some ideas to extend your learning:

Fine-tuning the ViT Mo del: Experiment with different hyperparameters in the ViT model to potentially improve performance.
Data Augmentation: Explore techniques like random cropping or flipping images to increase the dataset size virtually and improve model robustness.
Pre-trained ViT Models: Investigate pre-trained ViT models specifically designed for facial recognition tasks.These models can