# Facial expression recognition based on local region specific features and support vector machines

arXiv: https://arxiv.org/abs/1604.04337 · HTML: https://mlsanity.com/paper/1604.04337/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [FaceTopoNet: Facial Expression Recognition using Face Topology Learning](https://mlsanity.com/paper/2209.06322/) (2022)
- [Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks](https://mlsanity.com/paper/2202.00102/) (2022)
- [Facial Emotion Recognition: A multi-task approach using deep learning](https://mlsanity.com/paper/2110.15028/) (2021)
- [Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal](https://mlsanity.com/paper/2106.01467/) (2021)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)

## Abstract (arXiv)

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.