Journal Article

AI-Driven Deep Learning Architectures for Robust Emotion Recognition

Hamad Ali Abosaq

2025

Overview

Due to an insufficient labeled dataset, class-level variation emotion recognition becomes a challenging task in computer vision. Deep learning (DL) makes it possible to automatically learn meaningful patterns from facial expressions. It captures simple details such as edges, textures at low layers, and gradually builds up to more complex information, including Facial components and the overall meaning of the expression. Despite progress made via end-to-end learning, partial occlusions, inconsistent lighting, and biases within datasets are a few challenges that still remain. In this work, a DL based model is presented to classify two emotional states of human expression. The pipeline depends on several components, including the preparation of data, preprocessing and analysis, and the use of pretrained networks, dimensionality-reduction techniques, and region-based explanation via Grad-CAM. More than 2,000 images of happy and sad faces were derived from Kaggle. These images were used to test a custom-designed CNN and two widely adopted architectures, such as VGG16 and MobileNetV. The custom model attained an accuracy rate of 66% and 67% F1, while the VGG16 performed notably better with 78% accuracy and 77% F1, and the MobileNetV architecture, which achieved 77% accuracy and 73% F1. The statistical comparisons using paired t-tests and Wilcoxon signed-rank tests further confirmed these findings, showing that pre-trained models outperformed a custom CNN with a meaningful effect size. Although deeper networks are more susceptible to overfitting and the hand-crafted CNN suffered exhibited underfitting, the results indicate that pretained architecture provides a clear advantage for facial emotion recognition. This study makes a major contribution to existing computer vision research in removing the trade-off between accuracy and generalization, and opens doors to the application of lightweight yet interpretable models in practical affective computing systems.

• Publisher: Science and Information (SAI) Organization Limited
• Subject: Accuracy / Affective computing / Artificial neural networks / Computer architecture / Computer vision / Datasets / Deep learning / Emotion recognition / Emotional factors / Emotions / Machine learning / Rank tests