Original scientific paper
https://doi.org/10.1080/00051144.2024.2343964
Deep neural network-based emotion recognition using facial landmark features and particle swarm optimization
S. Vaijayanthi
orcid.org/0000-0002-1959-7906
; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
*
J. Arunnehru
; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
* Corresponding author.
Abstract
Relating specifically to human–computer interaction (HCI), computer vision research has placed
a substantial emphasis on intelligent emotion recognition in recent years. The primary emphasis
lies in investigating speech aspects and bodily motions, while the knowledge of recognizing emotions from facial expressions remains relatively unexplored. Automated facial emotion
detection allows a machine to assess and understand a person’s emotional state, allowing the
system to predict intent by analyzing facial expressions. Therefore, this research provides a
novel parameter selection strategy using swarm intelligence and a fitness function for intelligent recognition of micro emotions. This paper presents a novel method based on geometric
visual representation obtained from facial landmark points. We employ the Deep Neural Networks (DNN) model to analyze the input features from the normalized angle and distance values
derived from these landmarks. The results of the experiments show that Particle Swarm Optimization (PSO) worked very well by using only a few carefully chosen features. The method
achieved a recognition success rate of 98.76% on the MUG dataset and 97.79% on the GEMEP
datasets.
Keywords
Deep neural network; particle swarm optimization; facial motion recognition; geometrical features
Hrčak ID:
326261
URI
Publication date:
22.4.2024.
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