Facial Expression Recognition Dataset

Project Overview:

Objective

We wanted to make a dataset that helps develop better facial expression recognition technology, making emotion analysis more accurate and efficient.

Scope

We collected and annotated a lot of data to create this dataset. Our focus was on capturing many different facial expressions from different types of people to make sure the dataset works well in real-life situations.

Facial Expression Recognition Dataset
Facial Expression Recognition Dataset
Facial Expression Recognition Dataset
Facial Expression Recognition Dataset

Sources

  • Real Human Interactions: We took pictures of people‚Äôs faces showing different emotions. These people were from different ages, backgrounds, and moods.
  • Simulated Scenarios: We also took pictures of actors acting out different emotions in staged scenes to add more variety to the dataset.
  • Public Datasets: We included some facial expression data that was already available to make our dataset more diverse.
Facial Expression Recognition Dataset
Facial Expression Recognition Dataset

Data Collection Metrics

  • Total Data Collected: 20,000 facial expression instances.
  • Data Annotated for ML Training: 30,000 expressions

Annotation Process

Stages

  1. Expression Categories: We sorted expressions into basic emotions like happiness, sadness, anger, surprise, disgust, and fear.
  2. Intensity Rating: Each expression was given a score to show how strong the emotion was, helping to understand emotions better.
  3. Demographic Tags: We added information about people’s backgrounds to the data to make it useful for different purposes.

Annotation Metrics

  • Annotated Expressions: 30,000
  • Categorization Labels: 30,000
  • Intensity Labels: 20,000
Facial Expression Recognition Dataset
Facial Expression Recognition Dataset
Human Face Emotions
Facial Expression Recognition Dataset

Quality Assurance

Stages

Ongoing Model Testing: We kept checking how well the machine learning models recognized and understood facial expressions.
Privacy Rules: We followed strict privacy laws when collecting data, making sure participants agreed to it and keeping their information anonymous.
Improvement Process: We used feedback from early model results to make the dataset better and improve how we trained the models.

QA Metrics

  • Expression Recognition Accuracy: 93%
  • Intensity Detection Accuracy: 89%
  • Privacy Compliance: 100%

Conclusion

In conclusion, the Facial Expression Recognition Dataset has greatly moved forward emotion recognition technology. It accurately detects and understands various facial expressions, making it a vital tool for developers and researchers working on more empathetic and interactive AI systems.

quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

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

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

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

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Facial Expression Recognition Dataset