# What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors

arXiv: https://arxiv.org/abs/2109.05457 · HTML: https://mlsanity.com/paper/2109.05457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 91 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [FERAtt: Facial Expression Recognition with Attention Net](https://mlsanity.com/paper/1902.03284/) (2019)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields](https://mlsanity.com/paper/1703.06995/) (2017)
- [Robust Facial Expression Classification Using Shape and Appearance Features](https://mlsanity.com/paper/1505.04030/) (2015)

## Abstract (arXiv)

One of the most common problems encountered in human-computer interaction is automatic facial expression recognition. Although it is easy for human observer to recognize facial expressions, automatic recognition remains difficult for machines. One of the methods that machines can recognize facial expression is analyzing the changes in face during facial expression presentation. In this paper, optical flow algorithm was used to extract deformation or motion vectors created in the face because of facial expressions. Then, these extracted motion vectors are used to be analyzed. Their positions and directions were exploited for automatic facial expression recognition using different data mining techniques. It means that by employing motion vector features used as our data, facial expressions were recognized. Some of the most state-of-the-art classification algorithms such as C5.0, CRT, QUEST, CHAID, Deep Learning (DL), SVM and Discriminant algorithms were used to classify the extracted motion vectors. Using 10-fold cross validation, their performances were calculated. To compare their performance more precisely, the test was repeated 50 times. Meanwhile, the deformation of face was also analyzed in this research. For example, what exactly happened in each part of face when a person showed fear? Experimental results on Extended Cohen-Kanade (CK+) facial expression dataset demonstrated that the best methods were DL, SVM and C5.0, with the accuracy of 95.3%, 92.8% and 90.2% respectively.