# Recognition of facial expressions based on salient geometric features and support vector machines

arXiv: https://arxiv.org/abs/1604.04334 · HTML: https://mlsanity.com/paper/1604.04334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition](https://mlsanity.com/paper/2501.08199/) (2025)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study](https://mlsanity.com/paper/2104.02653/) (2021)

## Abstract (arXiv)

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.