Facial Expression Recognition Dataset for Improved Analysis

Facial Expression Recognition Dataset

Project Overview

Our goal was to create a detailed dataset to improve facial expression recognition systems. Named the Facial Expression Recognition Dataset, it aimed to help machine learning models accurately identify and understand human emotions based on facial expressions.

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.

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Sources

  • Real Human Interactions: We took pictures of people’s 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.
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Data Collection Metrics

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

Annotation Process

  • Expression Categories: We sorted expressions into basic emotions like happiness, sadness, anger, surprise, disgust, and fear.
  • Intensity Rating: Each expression was given a score to show how strong the emotion was, helping to understand emotions better.
  • 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
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Quality Assurance

  • 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.

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    Quality Data Creation
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    Guaranteed
    TAT
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    ISO 9001:2015, ISO/IEC 27001:2013 Certified
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    HIPAA
    Compliance
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    GDPR
    Compliance
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    Compliance and Security

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Facial Expression Recognition Dataset