# Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine
> Lawi A.

URL kanonis: https://discover.unhas.ac.id/publications/facial-expression-recognition-using-multiclass-ensemble-least-square-support-vec
Jurnal / Konferensi: Journal of Physics Conference Series
Tahun terbit: 2018
DOI: https://doi.org/10.1088/1742-6596/979/1/012032
ISSN: 17426588
Citations: 5

## Authors
- Lawi A.

## Abstract
Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person's mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

## Keywords
- Support vector machine
- Pattern recognition (psychology)
- Artificial intelligence
- Computer science
- Multiclass classification
- Square (algebra)
- Facial expression
- Speech recognition
- Facial expression recognition
- Expression (computer science)
- Machine learning
- Mathematics
- Facial recognition system
- Geometry
- Programming language

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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