Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

Facial expressions are the main ways how humans display emotions. Under certain circumstances, humans can do facial expression, but emotions can also appear in the special form of micro-expressions. A micro-expression is a very brief facial expression faced on people’s faces under some circumstances. Micro-expressions are shown in the situations when a person tries to lie or hide something. Studying micro-expressions sounds very attractive but considering the number of pixels that an image contains becomes difficult. Feature extraction techniques are the most popular ones for reducing data dimensionality. Those techniques create a new low-dimensional dataset, which tries to represent as much information as original dataset. Many and many methods are used for dimensionality reduction. Restricted Boltzmann Machine (RBM), Kernel Principal Component Analyses (KPCA) and t-distributed stochastic neighbor embedding (t-SNE) are currently widely used by researchers. Choosing the right dimensionality reduction technique is time consuming. This study proposes one framework for micro-expression recognition. The two key processes of this framework are the facial feature extraction (Dlib) and dimensionality reduction using RBM, KPCA and t-SNE. We will select the technique that generates new dataset which represents as much the original dataset as possible. The framework will be trained with images from the CASMEII database, which is a database built specially for research purposes. The framework will be tested with new images unseen before. Software used for conducting the experiments is Python.

@article{Bakiasi2024,
  title     = {Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Viola Bakiasi and Markela Muça and Rinela Kapçiu},
  doi       = {10.14569/IJACSA.2024.0150135},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150135}
}