RADIUS BASED BLOCK LOCAL BINARY PATTERN ON T-ZONE FACE AREA FOR FACE RECOGNITION

Md Jan Nordin,R U Gobithaasan,Abdul Aziz K Abdul Hamid,Sumazly Ulaiman

doi:10.3844/jcssp.2014.2525.2537

Abstract

This study presents a comparison of recognition performance between feature extraction on the T-Zone face area and Radius based block on the critical point. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. On the other hand, while the original LBP techniques focus in dividing the whole image into certain regions, we proposed a new scheme, which focuses on critical region, which gives more impact to the recognition performance. This technique is known as Radius Based Block Local Binary Pattern (RBB-LBP). Here we focus on three main area which is eye (including eyebrow), mouth and nose. We defined four critical point represent left eye, right eye, nose and mouth, from this four main point we derived the next nine point. This approach will automatically create the redundancy in various regions and for every radius size window a robust histogram with all possible labels constructed. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. RBB-LBP obtained high recognition rates when compared to standard LBP, LBP+PCA and also on T-Zone area. Our result shows of 16% improvement compared with LBP+PCA and 6% improvement compared with LBP. Our studies proves that the RBB-LBP method, reduce the length of the feature vector, while the recognition performance is improved.

Highlights

Operates on a closed-set scenario, while authentication
While the original Local Binary Pattern (LBP) techniques focus in dividing the whole image into certain regions, we proposed a new scheme, which focuses on critical region, which gives more impact to the recognition performance
Radius Based Block Local Binary Pattern (RBB-LBP) obtained high recognition rates when compared to standard LBP, LBP+Principle Component Analysis (PCA) and on T-Zone area

Summary

Introduction

Operates on a closed-set scenario (the individual to identify is present in the database), while authentication. Since the early 70’s, face recognition has these tasks are slightly different, both modes drawn the attention of researchers in various fields, which usually share the same classification algorithms In this include security, psychology, image processing and study, the focus is on the face authentication task. The general idea behind this global approach is to extract the main information in the training set as represented by some template images that capture most of the variability in the data. This is done by calculating the vectors which is the eigenvectors of the covariance matrix for the training set, that best represent this small region of image space. The main idea of PCA, used for face recognition, is that the original data is transformed to a different space, with fewer dimensions, in which it is easier to measure the relevant differences and similarities between them

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Conclusion