A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering

TL;DR

This paper introduces a novel face recognition method combining Eigenface feature extraction with a modified k-Means clustering algorithm, effectively recognizing faces with varying expressions without relying on traditional distance classifiers.

Contribution

It proposes a new nonparametric approach that integrates Eigenface and modified k-Means clustering for improved face recognition across different facial expressions.

Findings

Effective recognition of faces with different expressions

No reliance on conventional distance classifiers

Simulation results demonstrate robustness

Abstract

Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural human-machine interface. It may also be used in behavioural science. Although human can recognize the face practically without any effort, but reliable face recognition by machine is a challenge. This paper presents a new approach for recognizing the face of a person considering the expressions of the same human face at different instances of time. This methodology is developed combining Eigenface method for feature extraction and modified k-Means clustering for identification of the human face. This method endowed the face recognition without using the conventional distance measure classifiers. Simulation results show that proposed face recognition using perception of k-Means clustering is useful for face images with different facial expressions.