Performance Comparison of Facial Emotion Recognition: A Transfer Learning-Based Driver Assistance Framework for In-Vehicle Applications

Sahoo, Goutam Kumar; Das, Santos Kumar; Singh, Poonam

doi:10.1007/s00034-023-02320-7

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…VGG19 demonstrated a notable performance, achieving 99.7% accuracy on the KMU-FED database and competitive results across other benchmark datasets. Specifically, VGG19 attained performance accuracies of 98.98% for the CK+ dataset, 92.99% for the KDEF dataset with all data variations, 91.5% for the selected KDEF Frontal View dataset, 84.38% for JAFFE, 66.58% for FER2013, and 56.02% for SFEW [72]. Bialek et al explored emotion recognition through convolutional neural networks (CNNs), proposing various models including custom and transfer learning types, as well as ensemble approaches, alongside FER2013 dataset modifications.…”

Section: Badrulhisham Et Al Focused On Real-time Fer Employing Mobilenetmentioning

confidence: 99%

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Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Chowdhury,

Liu,

Ramanna

2024

Algorithms

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Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

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Section: Badrulhisham Et Al Focused On Real-time Fer Employing Mobilenetmentioning

confidence: 99%

“…Puthanidam [61] Hybrid CNN 89.58% Chen et al [62] ACD 99.12% Dar et al [65] Efficient-SwishNet 88.3% Fei et al [68] MobileNet + SVM 86.4% Mahesh et al [73] Feed-Forward Network 88.87% Sahoo et al [72] Pre-trained VGG19 93% Proposed Model…”

Section: Literature Type Accuracymentioning

confidence: 99%

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Chowdhury,

Liu,

Ramanna

2024

Algorithms

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“…VGG19 demonstrated notable performance, achieving 99.7% accuracy on the KMU-FED database and competitive results across other benchmark datasets. Specifically, VGG19 attained performance accuracies of 98.98% for the CK+ dataset, 92.99% for the KDEF dataset with all data variations, 91.5% for the selected KDEF FrontalView dataset, 84.38% for JAFFE, 66.58% for FER2013, and 56.02% for SFEW [55]. Additionally, some researchers have explored visual emotion recognition through social media images by employing pre-trained VGG19, ResNet50V2, and DenseNet-121 architectures as their base.…”

Section: Related Workmentioning

confidence: 99%

Simple Histogram Equalization Technique Improves Performance of VGG models on Facial Emotion Recognition Datasets

Chowdhury,

Liu,

Ramanna

2024

Preprint

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Facial Emotion Recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This paper investigates the impact of histogram equalization, data augmentation and model optimization strategies on three well-known FER datasets using pre-trained VGG models. Additionally, this paper showcases the effectiveness of different regularization techniques, callbacks, and learning schedulers in enhancing model performance by conducting extensive experiments. The model evaluation is discussed in terms of the following metrics: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG models exhibit state-of-the-art performance compared to more complex conventional transfer learning models resulting in accuracies of 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

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Non-local Graph Convolutional Network

Du,

Shao,

Tang

et al. 2023

Circuits Syst Signal Process

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Performance Comparison of Facial Emotion Recognition: A Transfer Learning-Based Driver Assistance Framework for In-Vehicle Applications

Cited by 7 publications

References 55 publications

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Simple Histogram Equalization Technique Improves Performance of VGG models on Facial Emotion Recognition Datasets

Non-local Graph Convolutional Network

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