چکیده (انگلیسی):

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.