基于2DLDA和LBP特征融合的人脸识别

Binbin Wang, Xinjie Hao, Lisheng Chen, Jingmin Cui, Lei Yunqi
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引用次数: 5

摘要

为了研究人脸识别算法在复杂光照、面部表情和姿态条件下的鲁棒性,从多个现有人脸数据库中选取图像,构建了三个子集数据库(照明、表情和姿态子集)。通过在ORL和上述三种数据库上的实验,分别讨论了七种典型算法在提取全局特征和局部特征方面的优缺点。为了提高人脸识别率,提出了一种基于二维线性判别分析(2DLDA)和局部二值模式(LBP)特征融合的人脸识别算法。实验结果验证了两类特征的互补性和所提特征融合算法的有效性。
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Face recognition based on the feature fusion of 2DLDA and LBP
To study the robustness of face recognition algorithms on conditions of complex illumination, facial expression and posture, three subset databases (Illumination, Expression and Posture subsets) are constructed by selecting images from several existing face databases. Advantages and disadvantages of seven typical algorithms on extracting global and local features are discussed respectively through the experiments on ORL and the three databases mentioned above. To improve the recognition rate, an algorithm of face recognition based on the feature fusion of Two-Dimensional Linear Discriminant Analysis (2DLDA) and Local Binary Pattern (LBP) is proposed in this paper. The experimental results verify both the complementarities of the two kinds of feature and the effectiveness of the proposed feature fusion algorithm.
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