Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical study of realvideo performance across the internet
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Face detection and recognition of natural human emotion using Markov random fields
Personal and Ubiquitous Computing
Personalized Human Emotion Classification Using Genetic Algorithm
VIZ '09 Proceedings of the 2009 Second International Conference in Visualisation
Analytic network process for pattern classification problems using genetic algorithms
Information Sciences: an International Journal
Automatic recognition of lower facial action units
Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
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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.