The Emotional State Stress Classification Dataset is a curated collection of images categorized by emotional states, enabling advanced analysis of stress levels. The dataset is divided into two primary categories:
- Non-Stress: Includes emotions like happiness and neutrality.
- Stress: Includes emotions like sadness and anger.
This dataset is ideal for researchers and developers aiming to build machine learning models for stress detection, emotional analysis, and mental health monitoring. By providing a clear distinction between stress-related and non-stress-related emotions, it supports applications in psychology, healthcare, and AI-driven emotional recognition.
Key Features:
- Two Main Categories: Non-Stress (Happy, Neutral) and Stress (Sad, Angry).
- Emotion-Based Labels: Facilitates targeted analysis of stress-related emotions.
- High-Quality Images: Ensures precision in training machine learning models.
- Real-World Applications: Ideal for mental health monitoring, stress detection tools, and emotion recognition systems.
- Source: Derived from the CK+ and TFEID official datasets, ensuring reliability and quality.
This dataset is sourced from Kaggle.