Facial Expressions recognition Based on Principal Component Analysis (PCA)

TL;DR

This paper proposes a PCA-based method using Eigenfaces to recognize facial expressions under various challenging conditions, aiming to improve accuracy and robustness in expression recognition tasks.

Contribution

It introduces a PCA approach with Eigenfaces for facial expression recognition, addressing challenges like occlusion and illumination variations.

Findings

Effective recognition under different lighting conditions

Improved accuracy with PCA Eigenfaces method

Robustness against facial occlusions

Abstract

The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint. The process of recognizing facial expression is a hard task due to the several circumstances such as facial occlusions, face shape, illumination, face colors, and etc. This paper present a PCA methodology to distinguish expressions of faces under different circumstances and identifying it. Relies on Eigen faces technique using standard Data base images. So as to overcome the problem of difficulty to computers to identify the features and expressions of persons.