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Facial Expression Recognition Dataset

The dataset contains 199,955 annotated images from 28,565 individuals of diverse races, genders, and ages. It is a large-scale facial expression dataset designed to train expression recognition and emotion classification models, capturing rich facial features and real-world emotional expressions for improved facial recognition and computer vision systems.

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  • people
    28 565
  • images
    199 955
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Emotion Recognition

The dataset contains 199,955 annotated images from 28,565 individuals of diverse races, genders, and ages. It is a large-scale facial expression dataset designed to train expression recognition and emotion classification models, capturing rich facial features and real-world emotional expressions for improved facial recognition and computer vision systems.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Emotion Recognition
  • people
    28 565
  • images
    199 955

Dataset Info

Characteristic Data
Description Images of people for facial expression recognition
Data types Image
Tasks Face expression recognition, Computer Vision
Total number of files 199 955
Number of people 28 565
Number of files in a set 7 images for each people
Labeling Metadata (ID, nationality, gender, age, emotion, collecting scene)
Gender Male, Female
Ethnicity Asian, Mexicans, Caucasian, African
Collecting scene Indoor, outdoor
Age Teenagers, young adults, middle-aged, elderly
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Statistics

Distribution by ethnicity
Distribution by gender

Technical
Characteristics

Characteristic Data
Image extension jpg, jpeg, png
Accuracy of expressions naming is more than 97%
Device Phone
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Artificial Intelligence & Machine Learning

    Training Emotion Recognition Models

    Facial Expression Recognition Dataset provides rich training data for building AI systems that detect facial emotions. With thousands of images covering different emotions and facial features, researchers can improve recognition algorithms for tasks like emotion detection, expression analysis, and human–computer interaction, making systems more adaptive and human-like.

  • Healthcare & Psychology

    Analyzing Emotional Responses in Patients

    Clinicians and researchers use such datasets to study emotional expressions linked to mental health and well-being. By examining facial landmarks and neutral expressions it supports emotion recognition for detecting stress, depression, or anxiety. It enhances tools that monitor emotional responses in therapy, diagnostics, and behavioral science.

  • Security & Monitoring

    Enhancing Recognition Systems with Facial Emotions

    The dataset strengthens facial recognition and recognition systems by integrating expression recognition. Understanding real emotions and emotional responses helps improve face recognition technology in surveillance, access control, and identity verification. Systems trained on datasets comprising emotional expressions perform better under varied data collection conditions, including non-neutral faces.

  • Customer Experience & Market Research

    Understanding Consumer Behavior through Expression Analysis

    Businesses use the Facial Expression Recognition Dataset to analyze emotional responses during product testing and advertising campaigns. By capturing basic emotions like joy, anger, or surprise, companies gain insights into consumer engagement. This faces database supports expression analysis that drives data collection for improving products, services, and customer experiences.

FAQs

What does Facial Expression Recognition Dataset include?
It contains 199,955 images from 28,565 individuals, with each person contributing seven images. The data is supported by metadata labels including ID, nationality, gender, age, and emotion type, ensuring strong training value for expression recognition systems.
What types of emotions are represented in the dataset?
The dataset covers basic emotions such as happiness, anger, sadness, surprise, fear, disgust, and neutral expressions. These emotional expressions provide diverse training material for emotion recognition models.
What types of annotations are provided?
Annotations cover emotion categories, demographic details (age, gender, ethnicity), and collecting conditions (indoor/outdoor). This structured metadata improves training accuracy for facial recognition and expression analysis models.
Can I request a sample before purchasing?
Yes, a sample of the facial expression dataset can be provided on request. This allows you to check image quality, annotation accuracy, and diversity of emotions before committing to a full purchase.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model that provides free samples for testing and evaluation, while full datasets are available exclusively for purchase. This model ensures users can validate data quality and structure before acquiring the full facial expression dataset for advanced computer vision and machine learning research.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in full compliance with GDPR and other applicable data protection laws. Data is gathered only from legally permissible and ethical sources, ensuring that all personal or biometric information in datasets is handled responsibly and transparently.
How long does it take to receive the dataset?
Once a dataset request is submitted, Unidata contacts you to confirm details and documentation. After signing the required agreements and completing payment, a dataset is delivered within 3–10 business days.
Is it unique data?
Yes. Each image in the facial expression recognition dataset is uniquely captured and annotated, representing diverse nationalities, age groups, and emotional expressions. This diversity ensures rich training data for deep learning, feature extraction, and emotion classification models in computer vision applications.
Still have questions about using Unidata datasets? Read our user-guides

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