Facial emotion recognition via stationary wavelet entropy and particle swarm optimization

2022 ◽  
pp. 145-162
Author(s):  
Xiang Li ◽  
Junding Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Emotion Recognition ◽  
Particle Swarm ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Swarm Optimization ◽  
Wavelet Entropy

scholarly journals Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 8375-8385 ◽  
Author(s):  
Yu-Dong Zhang ◽  
Zhang-Jing Yang ◽  
Hui-Min Lu ◽  
Xing-Xing Zhou ◽  
Preetha Phillips ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Emotion Recognition ◽  
Cross Validation ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Support Vector ◽  
Biorthogonal Wavelet ◽  
Fuzzy Support Vector Machine ◽  
Wavelet Entropy

Research of Tent Map Based Chaotic Particle Swarm Optimization Algorithm for Emotion Recognition

2010 ◽  
Vol 143-144 ◽  
pp. 1280-1284
Author(s):  
Hai Ning Wang ◽  
Shou Qian Sun ◽  
Jian Feng Wu ◽  
Fu Qian Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Emotion Recognition ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Feature Reduction ◽  
Experimental Simulation ◽  
Swarm Optimization ◽  
Tent Map ◽  
Chaotic Particle Swarm Optimization

For the problem of feature redundancy of emotion recognition based on multi-channel physiological signals and low efficiency of traditional feature reduction algorithms on great sample data, a new chaotic particle swarm optimization algorithm (TM-CPSO) was proposed to solve the problem of emotion feature selection by combining tent map based chaos search mechanism and improved particle swarm optimization algorithm. The problem of falling into local minimum can be avoided by mapping the search process to the recursive procedure of the chaotic orbit. The recognition rate and efficiency was increased and the algorithm's validity was verified through the analysis of experimental simulation data and the comparison of several recognition methods.

scholarly journals Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3028 ◽  
Author(s):  
Zina Li ◽  
Lina Qiu ◽  
Ruixin Li ◽  
Zhipeng He ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Average Accuracy

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.

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