Real-Time Recording and Analysis of Facial Expressions of Video Viewers

Abstract

Automated facial emotion recognition (AFER) is a technique with rising usage across a range of practical real-world applications ranging from security to advertising. AFER can be used to assess the emotional state of patients with mental health conditions, such as depression or anxiety to guide treatment decisions. Companies can use facial emotion recognition to gauge consumer reactions to different products or advertisements, providing valuable insights for product development and marketing strategies. The following paper examines the concept of facial emotion recognition using AI-based models and compares the results of two different techniques. The first technique uses face emotion detection using haar cascade classifier along with convolutional neural networks (CNN), and the second technique uses face emotion detection using facial landmarks along with CNN.