FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS DOI

Manisha Balkrishna Sutar,

Asha Ambhaikar

Biomedical Engineering Applications Basis and Communications, Journal Year: 2024, Volume and Issue: 36(05)

Published: July 17, 2024

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches data with existing databases to analyze individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, occlusions, different head positions, deformed faces, motion blur under unrestricted settings, complicated backgrounds make it complex images. In this paper, formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model utilized detection which JAFFE RAVDESS expression datasets are used. DCNNs proficient built-in feature extraction strategies from images map various expressions corresponding states adopted addition, intensity, directional, edge patterns as well correlation extracted utilizing hybrid textual pattern, RESNET 101 VGG 16-based modules assist DCNN attain informative high-resolution Further, optimization (FSO) incorporated effectively tunes capture relationships between learned excel FER capability. Evaluating metrics, face using dataset achieves notable efficiencies during training percentage (TP) of 90%, values 97.51%, 95.48%, 99.55%, 97.48%, 96.47%, minimum loss 2.49%. Simultaneously, demonstrates robust metric 96.75%, 98.49%, 95.01%, 96.72%, 97.59%, 3.25%. Finally, obtained results reveal efficacy FSO-opt DCNN, particularly in tasks, outperforms models across datasets, showcasing its versatility potential analysis applications.

Language: Английский

Coordinated optimization of control parameters for improving the stability of wind-PV hybrid power systems under improved pelican optimization algorithm DOI
Peng Liu, Yuchao Wu, Junwei Sun

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 7, 2024

Language: Английский

Citations

1
Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals DOI Creative Commons
Amita Dessai, H. G. Virani

Sci, Journal Year: 2024, Volume and Issue: 6(1), P. 10 - 10

Published: Feb. 4, 2024

Emotion classification using physiological signals is a promising approach that likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and Galvanic Skin Response (GSR) are more reliable than facial voice recognition because they not influenced by participant’s subjective perception. However, precision of emotion with ECG GSR satisfactory, new methods need be developed improve it. In addition, fusion time frequency features should explored increase accuracy. Therefore, we propose novel technique for exploits early extracted data in AMIGOS database. To validate performance model, used various machine learning classifiers, Support Vector Machine (SVM), Decision Tree, Random Forest (RF), K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives highest accuracy Valence Arousal, 69% 70% 96% 94% GSR, respectively. mutual information feature selection outperformed other Interestingly, was higher ECG, indicating preferred modality detection. Moreover, significantly enhances comparison ECG. Overall, our findings demonstrate proposed model based on multiple modalities suitable classifying emotions.

Language: Английский

Citations

0
Thermal error prediction and optimal design of cooling structure for oscillating head housing DOI Creative Commons
Zhaolong Li,

Junming Du,

Benchao Sun

et al.

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 61, P. 104963 - 104963

Published: Aug. 13, 2024

Language: Английский

Citations

0
Automated Emotion Recognition from Facial Expressions using Convolutional Neural Network DOI
S. A. Hussain,

N. S. Reddy,

Junnuthula Srivardhan

et al.

Published: Oct. 23, 2024

Language: Английский

Citations

0
FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS DOI

Manisha Balkrishna Sutar,

Asha Ambhaikar

Biomedical Engineering Applications Basis and Communications, Journal Year: 2024, Volume and Issue: 36(05)

Published: July 17, 2024

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches data with existing databases to analyze individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, occlusions, different head positions, deformed faces, motion blur under unrestricted settings, complicated backgrounds make it complex images. In this paper, formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model utilized detection which JAFFE RAVDESS expression datasets are used. DCNNs proficient built-in feature extraction strategies from images map various expressions corresponding states adopted addition, intensity, directional, edge patterns as well correlation extracted utilizing hybrid textual pattern, RESNET 101 VGG 16-based modules assist DCNN attain informative high-resolution Further, optimization (FSO) incorporated effectively tunes capture relationships between learned excel FER capability. Evaluating metrics, face using dataset achieves notable efficiencies during training percentage (TP) of 90%, values 97.51%, 95.48%, 99.55%, 97.48%, 96.47%, minimum loss 2.49%. Simultaneously, demonstrates robust metric 96.75%, 98.49%, 95.01%, 96.72%, 97.59%, 3.25%. Finally, obtained results reveal efficacy FSO-opt DCNN, particularly in tasks, outperforms models across datasets, showcasing its versatility potential analysis applications.

Language: Английский

Citations

0