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Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition

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Abstract

Face recognition applications focus on local features to prevent detailed information from being omitted while the feature extraction processes. This paper is based on presenting a local pattern-based model to extract more discriminative features that lead to more accurate classification. In local pattern-based feature extraction, the LBP is one of the most important approaches that many variants of this method have been proposed till now. LBP calculation is based on differences between the central pixel and the desired one. In contrast, the information hidden in the selected pixel’s neighborhood pixels is not included in this process. This paper proposes the DR_LBP approach to address this failure by defining distances and using some of them in a ratio form. Successful results have been earned in many experimental results. In LBP, the calculations’ primary flow takes advantage of two pixels in the LBP box, the central and the desired pixel. Contrary to the original LBP, this paper’s proposed approach uses three pixels of LBP box to conduct the feature vector, which leads to employing the information hidden in the relationship between neighboring pixels. This approach applies the experiments on two standard datasets, ORL Yale face and Faces94 dataset. The accuracy percent of the proposed plan is 95.95, 94.09 and 98.01 on ORL, Yale face and Faces94 dataset, respectively, which is the reason to present this model as a new face feature extraction approach.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Saeed Najafi Khanbebin & Vahid Mehrdad

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  1. Saeed Najafi Khanbebin
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  2. Vahid Mehrdad
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Correspondence to Vahid Mehrdad.

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Najafi Khanbebin, S., Mehrdad, V. Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition. Neural Comput & Applic 33, 7691–7707 (2021). https://doi.org/10.1007/s00521-020-05512-3

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  • DOI: https://doi.org/10.1007/s00521-020-05512-3

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