Real-Time Facial Emotion Recognition for Visualization Systems

EasyChair Preprint 8395

Abstract

This project aims to review the most popular deep learning algorithms and their performances in camera systems based on real-time facial emotion recognition and suggest a new model for future applications. Firstly, convolutional neural network (CNN) algorithms that recognize human emotions, such as AlexNet, GoogleNet, and VGG19, are investigated according to their performances. Then, the CNN algorithm with the best numerical performance is chosen for enhancement. After, the new hybrid model is constructed via chosen CNN and long short-term memory (LSTM). Lastly, the proposed model and face images achieved from the camera are combined to simulate real-time application.

Keyphrases: CNN, LSTM, face detection, facial expression, hybrid mode, recognition

@booklet{EasyChair:8395,
  author    = {Ceren Ozkara and Pınar Oğuz Ekim},
  title     = {Real-Time Facial Emotion Recognition for Visualization Systems},
  howpublished = {EasyChair Preprint 8395},
  year      = {EasyChair, 2022}}