Particle Swarm Optimization Algorithm for Facial Image Expression Classification

Vijayarani, S.; Priyatharsini, S.

doi:10.14257/ijsip.2016.9.9.02

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…From the collated results presented in Table VIII, it can be seen that the overall average performance of each proposed algorithm variant outperforms the average performance of other research in the field. It should also be noted that Jain et al out performs all proposed algorithm variants for Happiness (1), Surprise (4) and Fear (5). The results presented by Jain et al demonstrate a relatively high variance (9.3%) and a lack of consistent performance across the all emotions.…”

Section: Discussionmentioning

confidence: 94%

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Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Robson

Zhang

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Particle Swarm Optimization (PSO) has become a popular method of feature selection in classification problems, due to its powerful search capability and computational simplicity. Classification problems, such as facial emotion recognition, often involve data sets containing high volumes of features, not all of which are useful for classification. Redundant and irrelevant features have the potential to negatively impact the performance and accuracy of facial emotion recognition systems. The feature selection process identifies the most relevant features to achieve improved classification performance. While the use of PSO as a feature selection method in facial emotion recognition systems has seen some successes, it is still susceptible to the issue of premature convergence. This work presents seven PSO variants which mitigate against the premature convergence problem through the incorporation of three random probability distributions (Cauchy, Gaussian and Lévy). At each iteration of the proposed PSO models, probability distributions are used to increase search diversity and reduce the number of redundant features used for classification. The seven PSO variants presented in this study have demonstrated positive results when tested on real world data sets, outperforming the standard PSO model and other related work within the field.

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Section: Discussionmentioning

confidence: 94%

“…PSO has established itself as a powerful and widely used technique in classification problems across a variety of domains [1][2][3][4][5][6], due to its computational simplicity and powerful search capabilities. This includes implementations in feature selection systems designed to remove redundant and irrelevant features and improve classification efficiency.…”

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confidence: 99%

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Robson

Zhang

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases

Khaleel

Aziz

2021

J. Phys.: Conf. Ser.

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Artificial Intelligence Techniques, and image processing are playing a major role in medical science. In this paper, several methods of artificial intelligence techniques were used to diagnose Gingivitis disease. The Bat swarm algorithm, the Self-Organizing Map(SOM) algorithm and the Fuzzy Self-Organizing Map (FSOM)network algorithm were used to diagnose Gingivitis disease. Also, was used the traditional algorithm, which is the Principal Component Analysis (PCA) algorithm, for Feature Extraction of Gingivitis disease images. We compute the diagnostic accuracy on this images dataset. Next, we compared the final results of these three methods used and applied to this data. In this paper the best of these methods is the BAT, because in testing state the BAT was obtained higher accuracy for diagnose of Gingivitis disease equal (97.942%).

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Particle Swarm Optimization Algorithm for Facial Image Expression Classification

Abstract: Abstract

Cited by 2 publications

References 4 publications

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases

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