Paper Key : IRJ************529
Author: Pathivada Varshini,Potnuru Pavithra,Jajimoggala Akhil,Pyla Sai Dhanush ,Dr. Golagani. A. V. R C. Rao
Date Published: 07 Apr 2024
Abstract
Facial emotion detection has gained significant attention in recent years due to its wide-ranging applications in various domains such as healthcare, security, and human-computer interaction. However, existing approaches often overlook crucial demographic factors such as age and gender, which can significantly influence the interpretation of facial expressions. In this research, we propose a novel framework that integrates age and gender insights into facial emotion detection using the Long Short-Term Memory (LSTM) algorithm. We leverage the sequential nature of facial expression data to capture temporal dependencies and enhance emotion recognition accuracy. Through comprehensive experiments on benchmark datasets, we demonstrate the effectiveness of our proposed approach in achieving robust and accurate facial emotion detection across diverse demographics. Keywords: Facial expressions, Facial emotion detection, Age, Gender, Long Short-Term Memory (LSTM) algorithm, Facial feature extraction, Emotion classification