Facial Recognition Dataset – Labeled Faces in the Wild

Project Overview:

Objective

The aim was to create a dataset that makes facial recognition systems better at recognizing faces accurately and quickly. This dataset includes a variety of real-life facial images to achieve this goal.

Scope

We collected and marked many different facial expressions and situations, paying attention to how they’re used in real life. The project recorded various facial characteristics from different groups of people to make sure the dataset works well in different situations.

Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild

Sources

  • Real Human Interactions: We took pictures of people showing natural facial expressions in different situations. These people were of different ages and came from different cultural backgrounds.
  • Simulated Scenarios: We included photos of actors acting out different emotions to add more variety to the dataset.
Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild

Data Collection Metrics

  • Total Data Collected: 40,000 images of facial expressions.
  • Data Annotated for ML Training: 50,000 images.

Annotation Process

Stages

  1. Expression Categories: We sorted expressions into simple emotions like happiness, sadness, fear, and surprise.
  2. Intensity Rating: Each picture was given a score to show how strong the emotion is, helping to understand it better.
  3. Demographic Tags: We added details about the people in the pictures, like age and gender, to make the data more useful for different facial recognition purposes.

Annotation Metrics

Annotated Expressions: 50,000
Categorization Labels: 50,000
Intensity Labels: 40,000

Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild
Facial Recognition Dataset – Labeled Faces in the Wild

Quality Assurance

Stages

Continuous Model Testing: We keep checking our models regularly to make them more accurate.
Privacy Rules: We followed strict privacy laws, making sure all data we collected was with permission and made anonymous.
Improvement Process: We used feedback from the models to keep making the dataset and training methods better.

QA Metrics

  • Expression Recognition Accuracy: 95%
  • Intensity Detection Accuracy: 92%
  • Privacy Compliance: 100%

Conclusion

The “Labeled Faces in the Wild” dataset has greatly improved facial recognition technology. It provides detailed information about different human expressions, which helps in making advances in AI-based emotional understanding and interactive systems.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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Facial Recognition Dataset – Labeled Faces in the Wild