Facial Emotion Recognition (FER) is the technology that analyses facial expressions from both static images and videos in order to reveal information on one's emotional state. Facial expressions can display personal emotions and indicate an individual's intentions within a social situation. They are extremely important to the social interaction of individuals. Background scenes in which faces are perceived provide important contextual information for facial expression processing.
Facial Emotion Recognition is an AI-based technology that helps to identify human faces in images. It uses facial expressions to identify parts of an image or video to determine age, gender and emotions. This technology can be applied to fields like security, biometrics, law enforcement, etc., for tracking and surveillance purposes.
Link: https://www.kaggle.com/datasets/aadityasinghal/facial-expression-dataset
The dataset consists of train and test sets, with each set containing images belonging to 7 distinct categories of human expression, namely angry, fear, disgust, sad, neutral, surprise and happy. The class "disgust" is the minority class as compared to all other classes.
The data consists of 48x48 pixel grayscale images of faces. The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image. The task is to categorize each face based on the emotion shown in the facial expression in to one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).
- Numpy
- Pandas
- Seaborn
- Matplotlib
- Scikit-learn
- Keras
- Tensorflow
- OpenCV
This dataset was prepared by Pierre-Luc Carrier and Aaron Courville, as part of an ongoing research project. They have graciously provided the workshop organizers with a preliminary version of their dataset.