Facial Gesture Recognition Using Correlation And Mahalanobis Distance

TL;DR

This paper reviews real-time facial gesture recognition techniques using correlation and Mahalanobis distance, addressing challenges in reliably identifying expressions across individuals for applications in HCI, security, and clinical fields.

Contribution

It introduces a method combining correlation and Mahalanobis distance for real-time facial gesture recognition across six universal emotions.

Findings

Effective recognition of facial expressions using the proposed method.

Addresses individual differences in expression display.

Potential applications in HCI, security, and clinical practice.

Abstract

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is a goal towards which much research effort has been devoted recently. Facial gesture recognition is one of the important component of natural human-machine interfaces; it may also be used in behavioural science, security systems and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. The face expression recognition problem is challenging because different individuals display the same expression differently. This paper presents an overview of gesture recognition in real time using the concepts of correlation and Mahalanobis distance.We consider the six universal emotional categories namely joy, anger, fear, disgust, sadness and surprise.