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Video Emotion Recognition Dataset

This dataset comprises video recordings of 1,003 participants with 11 facial emotions and 15 inner emotions, including MP4 files and JSON annotations (ID, emotions, timestamps, transcripts), designed for face emotion recognition, expression analysis, and computer vision tasks such as emotion classification, sentiment analysis, and human emotion detection systems.

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  • people
    1,000+
  • facial emotions
    11
  • inner emotions
    15
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Emotion Recognition

This dataset comprises video recordings of 1,003 participants with 11 facial emotions and 15 inner emotions, including MP4 files and JSON annotations (ID, emotions, timestamps, transcripts), designed for face emotion recognition, expression analysis, and computer vision tasks such as emotion classification, sentiment analysis, and human emotion detection systems.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Emotion Recognition
  • people
    1,000+
  • facial emotions
    11
  • inner emotions
    15

Dataset Info

Characteristic Data
Description Video of people for emotion recognition
Data types Video
Tasks Emotion recognition, Computer Vision
Number of people 1,003
Number of facial emotions 11
Number of inner emotions 15
Labeling Metadata (ID, facial and inner emotions, start & end timestamp, transcript)
Gender Male, Female
Ethnicity Asian, Caucasian, African
Age Young people, middle-aged, old-aged
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Statistics

Distribution by ethnicity
Distribution by gender

Technical
Characteristics

Characteristic Data
Video extension mp4
Extension of annotation file json
Accuracy of text transcription is over 85%
Device Camera or smartphone
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Human–Computer Interaction

    Improving User Experience Through Emotion Recognition

    Video Emotion Recognition Dataset helps developers train systems that can understand human emotions in real time. Using video clips that capture diverse facial expressions and emotional reactions, companies can design interfaces that adapt to user mood, improving engagement, satisfaction, and communication in human–computer interaction environments.

  • Healthcare and Therapy

    Supporting Emotional Well-Being Monitoring Systems

    This dataset enables healthcare providers and researchers to build emotion detection tools for mental health monitoring. By analyzing subtle facial emotions and expression changes across video recordings, AI models can detect signs of stress, depression, or anxiety, providing valuable insights for therapists and clinical professionals working on personalized emotional support.

  • Education and E-Learning

    Enhancing Learning Platforms with Emotion Analysis

    Educators use this dataset to develop AI systems that interpret students’ facial expressions during virtual classes. Through emotion analysis and recognition technology, learning platforms can detect confusion, boredom, or engagement levels in real time, helping teachers adapt lesson delivery and improve the overall learning experience through emotion-aware feedback systems.

  • Entertainment and Media Analytics

    Understanding Audience Emotions from Video Content

    Media analysts rely on the dataset to train algorithms that detect emotions while viewers watch films, ads, or games. This data supports sentiment analysis and emotion classification across detected faces in videos, allowing studios and marketers to understand audience reactions and create more emotionally resonant and impactful visual content.

FAQs

What is included in this dataset?
The dataset comprises over 1,000 high-quality video recordings of people expressing various emotions.
What types of annotations are provided?
Annotations are provided in JSON format and include metadata such as individual ID, facial emotion, inner emotion, timestamps, and transcript accuracy exceeding 85%. These detailed annotations allow developers to train precise emotion classification and detection systems.
How was the data collected?
All video data is collected by Unidata’s partners using cameras and smartphones under controlled conditions. This ensures balanced representation across gender, age, and ethnicity for training emotion recognition models with real-world variability in facial expressions and emotions.
Is it possible to request a custom dataset?
Yes. Unidata offers custom dataset creation services to match specific research or business needs. If you need video recordings or annotations for particular emotional expressions, demographic groups, or recognition systems, a tailored dataset can be developed on request.
What is Video Emotion Recognition Dataset used for?
This dataset is designed for emotion recognition and analysis in computer vision and deep learning applications. It supports training models that detect and classify human emotions, helping advance emotion-based AI systems such as recognition technology, sentiment analysis tools, and expression recognition models.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for testing, while full datasets require purchase. This approach ensures accessibility for evaluation while maintaining data integrity and controlled commercial use.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure, providing high availability and reliability. Our practices comply with ISO 27001 and ISO 27701 standards to ensure data security and privacy in handling video recordings, annotations, and emotional expression data.
How long does it take to receive the dataset?
Once your request is submitted, our team reviews the details and finalizes documentation. After signing and payment, delivery of the dataset typically takes between 3 and 10 days.
Still have questions about using Unidata datasets? Read our user-guides

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