Real Time Facial Recognition Using Principal Component Analysis (PCA) And EmguCV

S Sultoni,A G Abdullah

doi:10.1088/1757-899x/384/1/012079

Abstract

Facial recognition is a challenging research in the field of image processing and computer vision, especially for security systems, weight determiner, and emotional determination based on the face image recognition. Some of the methods that can be used in facial recognition are holistic, feature extraction, hybrids and intelligent systems. This paper used the method of characteristic extraction that used Principal Component Analysis (PCA) which was built using EmguCV application. The purpose of this research is to assess the accuracy of Principal Component Analysis (PCA) method when combined with Emgu CV in face recognition in real time. Based on the results of training and testing, it can be concluded that the PCA method combined with EmguCV has better accuracy, if the data used has the same characteristics, PCA and EmguCV can also be developed to make image processing application especially for security system, because it applies simple statistic method and easy- applied algorithm.

Highlights

Facial recognition is a challenging field of research in the field of image analysis and computer vision
Facial recognition is widely used for security systems [1], to determine a person's weight [2] and to know one's emotions [3]
Feature extraction methods is used in [7] [8] [9] [10] research using only parts of the face that are considered to have the most discriminant features used as training data and test data and which use hybrids are [11] [12] [13] [14] [17] is a study that combines holistic and feature extraction methods

Summary

Introduction

Facial recognition is a challenging field of research in the field of image analysis and computer vision. Feature extraction methods is used in [7] [8] [9] [10] research using only parts of the face that are considered to have the most discriminant features used as training data and test data and which use hybrids are [11] [12] [13] [14] [17] is a study that combines holistic and feature extraction methods. The most widely used facial recognition method is Principal Component Analysis (PCA) which is a feature extrusion technique with the aim to find the eigen value and egien vector aimed to find the feature value of the most discriminant face [1], the result of extraction feature between training data and test data compared using euclidean distance to know the measurement level of similarity. Experimental Methods The method applied in this research is displayed in the following chart

Results and Discussion

10 Person 10

Method

Conclusions