# A Real Time Facial Expression Classification System Using Local Binary Patterns

arXiv: https://arxiv.org/abs/1505.04058 · HTML: https://mlsanity.com/paper/1505.04058/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2015.

## Built on by (most cited first, 7 of 7)

- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition](https://mlsanity.com/paper/2205.11785/) (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)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)
- [Facial Expression Recognition Using Residual Masking Network](https://mlsanity.com/paper/2603.05937/) (2026)

## Abstract (arXiv)

Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns (LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.