Static Facial Expression In The Wild

Project Overview

The “Static Facial Expression in the Wild” project aims to make emotion recognition systems better by collecting a strong dataset of facial expressions from real-life situations. This dataset is important for teaching AI models to understand human emotions accurately in different everyday settings.

Objective

Our goal is to create a big dataset of natural facial expressions to help AI emotion recognition technology work more accurately and quickly.

Scope

This project involves gathering and labeling facial expressions from different real-life situations to capture the complexity of human emotions.

Sources

We collect facial expressions from various sources:

Surveillance Footage: Using CCTV and public cameras to catch spontaneous facial expressions.
Voluntary Submissions: People share their expressions through a secure platform, showing different emotions in different situations.
Partnered Public Events: We collaborate with events where people agree to have their expressions recorded.

Data Collection Metrics

Total Images Captured: 50,000
Surveillance Footage: 30,000
Voluntary Submissions: 15,000
Partnered Public Events: 5,000

Annotation Process

Finding Faces: Recognizing and labeling faces in each picture.
Sorting Expressions: Putting expressions into groups like happiness, sadness, surprise, anger, and more.
Measuring Intensity: Rating how strong each expression is to make the dataset more detailed.

Annotation Metrics:

Images with Facial Labels: 50,000

Expression Categorizations Completed: 50,000

Intensity Scored Expressions: 50,000

Quality Assurance

Annotation Review: Our team of experts carefully checks facial expressions to make sure they’re accurate.

Data Cleanup: We regularly clean up the dataset by removing unclear or poorly captured images.

Privacy Protection: We follow strict rules to keep your data safe and private.

QA Metrics

Reviewed Annotations: 5,000 (10% of total)

Data Cleansing Initiatives: Continuous improvement and validation.

Conclusion

In summary, the “Static Facial Expression in the Wild” dataset is a game-changer for improving AI’s grasp of human emotions in everyday situations. With its thorough annotations and detailed data, it paves the way for more accurate and sophisticated emotion recognition systems.