# Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

arXiv: https://arxiv.org/abs/1604.03225 · HTML: https://mlsanity.com/paper/1604.03225/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 10 of 10)

- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks](https://mlsanity.com/paper/2202.00102/) (2022)
- [MAFER: a Multi-resolution Approach to Facial Expression Recognition](https://mlsanity.com/paper/2105.02481/) (2021)
- [Facial Emotion Recognition: A multi-task approach using deep learning](https://mlsanity.com/paper/2110.15028/) (2021)
- [A Multi-resolution Approach to Expression Recognition in the Wild](https://mlsanity.com/paper/2103.05723/) (2021)
- [Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal](https://mlsanity.com/paper/2106.01467/) (2021)
- [GesSure- A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application](https://mlsanity.com/paper/2207.11033/) (2022)
- [Facial Expression Recognition Using Residual Masking Network](https://mlsanity.com/paper/2603.05937/) (2026)

## Abstract (arXiv)

Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.