Abstract：The image is segmented at different levels to extract the uniform local binary pattern (ULBP) histogram features of the sub-block images. The global and local features are taken into account, and meanwhile the processing space is converted from the gray space to ULBP feature subspace. Consequently, the correlation between row vectors can be eliminated effectively. Thus, the discriminant projection matrix is performed better through row two-dimensional linear discriminant analysis (R2DLDA). Experimental results on ORL, YALE and FERET databases show that compared with some common methods based on 2DLDA and multilevel LBP, the proposed method achieves a higher recognition rate with a low feature dimension, which proves its effectiveness.
吴煌鹏，戴声奎. 基于ULBP特征子空间的2DLDA人脸识别方法*[J]. 模式识别与人工智能, 2014, 27(10): 894-899.
WU Huang-Peng, DAI Sheng-Kui. Face Recognition of 2DLDA Based on ULBP Eigensubspace. , 2014, 27(10): 894-899.
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