Chowdhury, J.H.; Liu, Q.; Ramanna, S. Simple Histogram Equalization Technique Improves Performance of VGG models on Facial Emotion Recognition Datasets. Preprints2024, 2024041540. https://doi.org/10.20944/preprints202404.1540.v1
APA Style
Chowdhury, J.H., Liu, Q., & Ramanna, S. (2024). Simple Histogram Equalization Technique Improves Performance of VGG models on Facial Emotion Recognition Datasets. Preprints. https://doi.org/10.20944/preprints202404.1540.v1
Chicago/Turabian Style
Chowdhury, J.H., Qian Liu and Sheela Ramanna. 2024 "Simple Histogram Equalization Technique Improves Performance of VGG models on Facial Emotion Recognition Datasets" Preprints. https://doi.org/10.20944/preprints202404.1540.v1
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
