GPSO versus GA in facial emotion detection

Abstract

We recently proposed the guided particle swarm optimisation GPSO algorithm as a modification to the popular particle swarm optimisation PSO algorithm with the objective of solving the facial emotion recognition problem. A real-time facial emotion recognition software was implemented using GPSO and tested with 25 subjects. The result was found to be good both in terms of recognition success rate and recognition speed. As a follow-up, we decided to investigate how our novel GPSO approach compares with existing popular classification methods, such as genetic algorithm GA. We re-implement our emotion recognition software using GA and tested it using the video recordings of the same 25 subjects that were used to test the GPSO-based system. Our results show that while the recognition success rate achieved using GA is still reasonable, the recognition speed is very slow, suggesting that the GA method may not be suitable for real-time emotion recognition applications.