Deep Learning Based on Face Emotion Recognition using an Artificial Neural Network

Facial Emotion Recognition has become a critical research domain in Artificial Intelligence due to its vital applications across multiple fields, including security and healthcare. This study aims to overcome prevailing limitations of current methodologies, which primarily rely on static image frames and neutral facial expressions, hindering optimal emotion recognition rates. Leveraging advanced deep learning techniques, this research focused on the identification of seven human emotions—happiness, anger, disgust, fear, sadness, surprise, and neutrality—using both static and dynamic images from CK+ and FER datasets. The research experimented with various models such as Lightweight MobileNet and Artificial Neural Networks, aiming for enhanced accuracy in real-time emotion recognition applications. A novel visualization technique was developed to pinpoint the crucial facial regions integral for detecting distinct emotions by analyzing classifier outputs, revealing that different emotions are sensitive to different facial areas. The results emphasize the value of specific facial regions in conveying and recognizing emotions and illustrate the substantial promise of deep learning methodologies in advancing Facial Emotion Recognition technology.

I. Introduction

Generally, People interact with each other of them in the form of language, gesture reactions, glints, and emotions. Computer System like can it recognizes that there is a great need for this in many areas. As for artificial intelligence, there will be a computer that can interact with people very naturally and can understand human emotions from faces. And it will be Assistance with counseling in other healthcare-related areas. However, many cases, constant emotion recognition is not reliable and accurate. It must know a people’s feeling for a moment in a real-time environment. Thus, the proposed model that is aimed at real time applications. For real-time face emotion recognition working on different areas like Human-machine interface [1], Security [2], [3] healthcare [4], [5] and animation [6].