Journal Article

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

Qicong WANG Binbin WANG Xinjie HAO Lisheng CHEN Jingmin CUI Rongrong JI Yunqi LEI

2016

Overview

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

• Publisher: Higher Education Press,Springer Nature B.V
• Subject: Algorithms / Computer Science / Decision analysis / decision fusion / Discriminant analysis / Face recognition / global feature / Illumination / linear discriminant analysis / local binary pattern / local feature / Research Article / Two dimensional analysis / 二值模式 / 二维 / 二进制模式 / 人脸识别算法 / 决策融合 / 局部特征 / 线性判别分析 / 面部表情