Facial Emotion Recognition and Detection in Python using Deep Learning

Abstract:

Humans frequently experience a wide range of emotions, and their facial expressions alter to reflect these changes. The ability to read other people’s emotions is a fundamental component of successful social interaction. Since ancient times, people have been interested in studying how to automatically recognize different emotions.

The user’s emotions, as determined by the user’s facial expression, will be analyzed by this deep learning system. Detection of the face in real time and interpretation of a variety of facial emotions, such as pleased, sad, angry, terrified, surprised, disgusted, and neutral, among others. This technology is able to recognize six distinct feelings that may be experienced by humans.

The trained model has the capability of recognizing all of the aforementioned feelings in real time. In order to function well, an autonomous facial expression recognition system needs to be able to recognize and locate faces even when there is a lot of clutter present, extract facial features, and categorize facial expressions.

The Convolutional Neural Network (CNN) is being used to forcefully victimize people for the face expression detection system. On the FER2013 dataset, a CNN model is being trained. This entire project makes use of the FER2013 Kaggle faces expression dataset, which consists of six facial traits that are labeled as happy, sad, surprised, afraid, angry, disgusted, and neutral respectively.

In comparison to the previous datasets, FER contains photographs with a greater variety of characteristics, such as face occlusion, partial faces, images with poor contrast, and spectacles. This system has the capability to monitor the emotions of people, to differentiate between emotions and accurately label them, and to utilize the knowledge gleaned from monitoring those emotions to influence the thoughts and behavior of a specific individual.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.