Human Emotional Facial Expression Recognition

TL;DR

This paper presents an automatic facial expression recognition system combining an Adaboost face detector, manifold learning-based feature selection, and a synergetic prototype classifier, achieving effective performance efficiently.

Contribution

It introduces a novel combination of feature selection and classification methods specifically designed for facial expression recognition.

Findings

Effective facial expression recognition performance

Reduced processing time compared to existing methods

Improved feature selection enhances accuracy

Abstract

An automatic Facial Expression Recognition (FER) model with Adaboost face detector, feature selection based on manifold learning and synergetic prototype based classifier has been proposed. Improved feature selection method and proposed classifier can achieve favorable effectiveness to performance FER in reasonable processing time.