Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique

TL;DR

This paper presents a facial expression recognition system using eigenvector features extracted from significant facial regions and a Euclidean distance-based decision method, achieving accurate recognition of expressions.

Contribution

It introduces a novel approach combining eigenvector features from key facial regions with Euclidean distance for expression recognition.

Findings

High recognition accuracy demonstrated

Effective feature extraction from facial regions

Robust decision-making using Euclidean distance

Abstract

In this paper, an Eigenvector based system has been presented to recognize facial expressions from digital facial images. In the approach, firstly the images were acquired and cropping of five significant portions from the image was performed to extract and store the Eigenvectors specific to the expressions. The Eigenvectors for the test images were also computed, and finally the input facial image was recognized when similarity was obtained by calculating the minimum Euclidean distance between the test image and the different expressions.