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.
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.
Data Collection Metrics
- Total Data Collected: 20,000 facial expression instances.
- Data Annotated for ML Training: 30,000 expressions
Annotation Process
Stages
- 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
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 Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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