Machine Learning Integration for Precise Facial Micro-Expression Recognition
- 1 Department Computer Science, Faculty of Information Technology, University of Durres, Albania
- 2 Department of Applied Mathematics, Faculty of Natural Science, University of Tirana, Albania
Abstract
This study advances facial micro-expression recognition through innovative machine-learning techniques, addressing critical needs in psychology, security, and human-computer interaction. The purpose of this study is to improve micro-expression recognition through the optimization of feature transformation and machine learning algorithms. We introduce a novel approach combining Kernel Principal Component Analysis (KPCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, paired with advanced classifiers (SVM, Random Forest, k-NN, Decision Trees) to enhance recognition accuracy of these subtle, rapid facial movements. This combination outperforms previous KPCA and t-SNE approaches in preserving both local and global structures of high-dimensional facial data. Our rigorous experimental design involved 28,175 samples from the AffectNet dataset (22,540 for training and 5,635 for validation), utilizing a combination of Kernel Principal Component Analysis (KPCA) with Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, followed by random forest classification to capture micro-expressions. Ethical standards, including informed consent and data protection, were strictly maintained throughout. The results show a marked improvement over traditional methods, with our top-performing model achieving 94% accuracy. Key contributions include The optimization of KPCA and UMAP for dimensionality reduction, achieving a state-of-the-art 94% accuracy with Random Forest classification on the AffectNet dataset; Significant computational efficiency gains, reducing training time while improving accuracy; Comprehensive quantitative comparisons of classification performance (accuracy, precision, recall, F1-score) across various model combinations; and Rigorous analysis of the impact of dimensionality reduction techniques on preserving essential micro-expression features. These advancements significantly push the boundaries of emotion recognition technology. This research has far-reaching implications, potentially revolutionizing lie detection, autism research, and human-robot interaction. Our findings pave the way for a more nuanced understanding of human emotions in various applications. The software used for the experiments was Python.
DOI: https://doi.org/10.3844/jcssp.2024.1545.1558
Copyright: © 2024 Viola Bakiasi Shtino, Markela Muça and Senada Bushati. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Affect Net Database
- Facial Micro-Expression Recognition
- Dimension Reduction Techniques
- Advanced Classification Models
- Optimization