A personal facial expression monitoring system using deep learning
Abstract
Description
A thesis Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in COMPUTER SCIENCE from Texas A&M University-Corpus Christi in Corpus Christi, Texas.
Facial expression recognition has been a challenge for many years. With the recent growth in machine learning, a real-time facial expression recognition system using deep learning technology can be useful for an emotion monitoring system for Human-computer interaction(HCI). We proposed a Personal Facial Expression Monitoring System (PFEMS). We designed a custom Convolutional Neural Network model and used it to train and test different facial expression images with the TensorFlow machine learning library. PFEMS has two parts, a recognizer for validation and a data training model for data training. The recognizer contains a facial detector and a facial expression recognizer. The facial detector extracts facial images from video frames and the facial expression recognizer distinguishes the extracted images. The data training model uses the Convolutional Neural Network to train data and the recognizer also uses Convolutional Neural Network to monitor the emotional state of a user through their facial expressions. The system recognizes the six universal emotions, angry, disgust, happy, surprise, sad and fear, along with neutral.
Computing Sciences
College of Science and Engineering
Facial expression recognition has been a challenge for many years. With the recent growth in machine learning, a real-time facial expression recognition system using deep learning technology can be useful for an emotion monitoring system for Human-computer interaction(HCI). We proposed a Personal Facial Expression Monitoring System (PFEMS). We designed a custom Convolutional Neural Network model and used it to train and test different facial expression images with the TensorFlow machine learning library. PFEMS has two parts, a recognizer for validation and a data training model for data training. The recognizer contains a facial detector and a facial expression recognizer. The facial detector extracts facial images from video frames and the facial expression recognizer distinguishes the extracted images. The data training model uses the Convolutional Neural Network to train data and the recognizer also uses Convolutional Neural Network to monitor the emotional state of a user through their facial expressions. The system recognizes the six universal emotions, angry, disgust, happy, surprise, sad and fear, along with neutral.
Computing Sciences
College of Science and Engineering