Deepfer: Camera Vision Based Automatic Facial Expression Analysis System Using Deep Learning Techniques

Deepfer: Camera Vision Based Automatic Facial Expression Analysis System Using Deep Learning Techniques

Authors- E. Ragul, Assistant Professor Dr. Lipsa Nayak

Abstract--Facial expressions are a powerful form of non-verbal communication and play a crucial role in assessing human emotions. This paper presents DeepFER, a camera vision-based automatic facial expression recognition system that leverages deep learning to analyze emotions in real time. The system uses a Deep Convolutional Neural Network (DCNN) to classify seven basic facial expressions—angry, disgust, fear, happy, sad, surprise, and neutral—captured through live or offline video feeds. A custom dataset of annotated facial expressions is used to train the DCNN model, ensuring high accuracy across varied lighting and environmental conditions. With its accurate classification capabilities and scalable architecture, DeepFER presents a significant step toward intelligent emotion-aware systems.

DOI: /10.61463/ijset.vol.13.issue2.436