Human Expression Recognition using Facial Shape Based Fourier Descriptors Fusion

TL;DR

This paper introduces a facial expression recognition method that combines Fourier shape descriptors of facial regions with SVM classification, achieving high accuracy despite challenges like illumination and occlusion.

Contribution

It proposes a novel fusion of Fourier shape descriptors for facial regions and applies SVM for improved expression recognition accuracy.

Findings

High recognition accuracy on facial expression datasets

Effective handling of occlusion and illumination issues

Robust multi-class classification results

Abstract

Dynamic facial expression recognition has many useful applications in social networks, multimedia content analysis, security systems and others. This challenging process must be done under recurrent problems of image illumination and low resolution which changes at partial occlusions. This paper aims to produce a new facial expression recognition method based on the changes in the facial muscles. The geometric features are used to specify the facial regions i.e., mouth, eyes, and nose. The generic Fourier shape descriptor in conjunction with elliptic Fourier shape descriptor is used as an attribute to represent different emotions under frequency spectrum features. Afterwards a multi-class support vector machine is applied for classification of seven human expression. The statistical analysis showed our approach obtained overall competent recognition using 5-fold cross validation with high accuracy on well-known facial expression dataset.