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multiscale discriminative binary pattern (MDBP): Novel Descriptor for Face Recognition in Pose and Illumination Variations

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IETE JOURNAL OF RESEARCH 2025年

作者： Karanwal, Shekhar GLA Univ Dept Comp Engn & Applicat Mathura Uttar Pradesh India

Local binary pattern (LBP) encodes the binary pattern by neighborhood comparison with a center pixel. In addition, various LBP variants follow the uniform coordination between neighbors and center pixel. The two major issues noticed in these descriptors are: (1) Limited spatial size (2) Noisy thresholding function. The proposed work eliminates both these issues and introduces a novel LBP variant for Face Recognition called multiscale discriminative binary pattern (MDBP). In MDBP, the center pixel is replaced by a novel thresholding function value, which is formed utilizing the two local statistics, mean and median, in conjunction with the introduced parameter t (the threshold). The thresholding function is written as [(Mean + Median)/abs (1-t)]. The mean and median are computed from the 5 x 5 patch. By putting 3 different values of t (i.e. 2.95, 3, and 3.05), 3 values are produced for comparison. Then 8 neighborhoods at 2 different scales (Specified as P = 8, R = 1, and P = 8, R = 2) are compared with 3 different values. Eventually, the multiscale features extracted utilizing the same compared values are combined to produce the size of 3 novel descriptors, MDBP1, MDBP2, and MDBP3. For all three proposed descriptors, the regional histograms are extracted and fused. PCA and FLDA are used for compression, and SVMs and NN are used for the classification of results. Tests are done on ORL, GT, Yale, and EYB datasets. The results prove the efficacy of MDBP against the compared ones.

关键词： Classification Feature compression Global feature Local binary pattern Local feature multiscale discriminative binary pattern

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