InternationalResearchJournal of Modernization in EngineeringTechnologyand Science

FACE RECOGNITION ALGORITHMS: A COMPARATIVE STUDY

Himanshu Dorbi*1, Prabhakar Joshi*2

*1,2DepartmentofComputerScience&EngineeringGraphicEraHillUniversity,DehradunUttarakhand,India

DOI:https://www.doi.org/10.56726/IRJMETS41255

Abstract

This research paper analyses Eigenfaces, Fisherfaces, KLT (Kanade-Lucas-Tomasi) and Viola-Jones face recognition algorithms. Examined under various conditions, including lighting changes, design changes, nonsolid deformations, occlusions, anomalies in the dataset, and image variations and resolution, the study examinesitslimitations,strengthsandfunctions.Thetruth,speaking,istogiveinsightintotheircomparisons. Useful tips for choosing an algorithm based on the situation. Eigenfaces and Fisherfaces perform well in a controlled environment with limited variability. Viola-Jones has demonstrated high accuracyin face detection and object detection. KLT is mainly used for feature monitoring and optical measurement. This outcome facilitatesa comprehensive understanding ofthestrengths andlimitationsof eachalgorithm,helpingto make informeddecisionsaboutfacialrecognitioninavarietyofsituations.

I. INTRODUCTION

Face recognition Facial recognition algorithms play an important role in many applications, from security systems to human-computer interaction. This research paper presents an analysis of four face recognition systems: Eigenfaces, Fisherfaces, KLT (Kanade-Lucas-Tomasi) and Viola-Jones. The aim is to evaluate their limitations, strengths and performances in different situations, including lighting changes, lighting changes, non-hard deformations, occlusions, dataset aberrations, image quality and resolution. Based on this analysis, this study aims to offer suggestions for the comparison of algorithms. It examines the accuracy achieved by eachalgorithmandhighlightsitsadvantagesanddisadvantages.Inaddition,recommendationswillbemadeto guidetheselectionalgorithmonthebasisofspecificconditionsandrequirements.Eigenfacesandherringbones based on PCA andFLDAtechniques, respectively,are particularlyuseful incontrol environments with limited variability. Known for its high accuracy in face detection and object recognition, Viola-Jones stands out in situations that require powerful detection capabilities. On the other hand, KLT is mainly used for feature trackingandvisualevaluation,notfacerecognition.Understandingtheuniquepropertiesofeachalgorithmand its performance in different situations is important for making informed decisions in facial recognition. Using these studies, doctors can choose the most appropriate algorithm that will be accurate and effective in their case.

II. METHODOLOGY

Someofthecommonlyusedfacerecognizingalgorithmareasfollows: Eigenfaces- EigenfacesisapopularfacerecognitionalgorithmthatwasintroducedbyMatthewTurkandAlex Pentland in 1991. It revolutionized the field of face recognition by employing the concept of principal componentanalysis(PCA)fordimensionalityreduction.

TheprinciplebehindtheEigenfacesalgorithmistorepresentfacesasalinearcombinationofeigenfaces,which aretheprincipalcomponentsobtainedthroughPCA.

PCA is a statistical technique that aims to capture the most significant variations in a dataset by projecting it ontoalower-dimensionalspace.MaintainingtheIntegrityoftheSpecifications.

TheEigenfacesalgorithmfollowsaseriesofstepstoperformfacerecognition:

● Data Collection: Collect a dataset of face images representing different individuals under various conditions.

● Preprocessing: Enhancefaceimagesthrough grayscale conversion, histogram equalization,andgeometric normalization.

● DimensionalityReduction:ApplyPCAtoreducethedimensionalityofpreprocessedfaceimages,extracting eigenfacesasprincipalcomponents.