Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

Isnawati Muslihah,Muqorobin Muqorobin

doi:10.29040/ijcis.v1i1.10

Abstract

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA.

Highlights

The face is the main identity used to recognize someone, this is what inspires so many research on face recognition for identity
The feature extraction process is very important in the pattern recognition phase because it is one of the things that determines the high level of face recognition
Several methods in the facial feature extraction process have been found, especially in this study comparing the characteristics of the texture produced by the Local Binary Pattern (LBP) and Local Ternary Pattern (LTP)

Summary

INTRODUCTION

The face is the main identity used to recognize someone, this is what inspires so many research on face recognition for identity. Several methods in the facial feature extraction process have been found, especially in this study comparing the characteristics of the texture produced by the Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). Based on the background described, to optimize the application of PLDA as a face recognition method, it was decided to conduct research using the LBP and LTP approaches for feature extraction, namely by using the texture and shape features of an image, converting grayscale images into binary numbers [5]. Based on the system design, the steps taken in the preprocessing data are cropping the image to get the ROI image (Region of Interest) and resize it to get the desired image size, feature extraction with LBP and LTP to get facial representation. The final accuracy of this test data shows the performance of the proposed recognition method

RESULT

Findings

CONCLUSIONS