Static Facial Expression In The Wild

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Project Overview

Objective

Our goal is to create a big dataset of natural facial expressions, specifically focusing on Static Facial Expression In The Wild, 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

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

Stages

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 Pest and Disease Annotations: 15,000
Metadata Logging: 15,000

Quality Assurance

Stages

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.