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Triangle and orthogonal local binary pattern for face recognition

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Abstract

LBP is known as one of the best performing local descriptor in texture representation. But there are various shortcomings observed in LBP and these are finite spatial patch and large feature size. These shortcomings also persist in numerous LBP variants. To remedy these shortcomings, the proposed work presents 2 LBP variants so-called Triangle LBP (TLBP) and Orthogonal LBP (OLBP), in pose and expression variations. TLBP features are extracted in horizontal and vertical directions by using 3 × 5 and 5 × 3 image patches, by rotating the triangle in 00 and 1800 directions of both patch. OLBP features are extracted from orthogonal positions of the respective patch. The feature size derived from TLBP and OLBP descriptors are fused to manufacture the robust face descriptor called as Triangle And Orthogonal LBP (TAO-LBP). The compressed set of feature is accomplished by PCA. The finite spatial patch problem is eliminated by using two different patches, from which lower and higher scale features are extracted in the form of histograms, by using novel methodologies as introduced in TLBP and OLBP. The large feature size problem is scrutinized by the deployment of PCA and FLDA, which also selects the relevant and essential information for classification. The classification is procured by SVMs and NN. Experiments confirms the potential of TAO-LBP against LBP-like and non LBP-like based methods on ORL, GT, JAFFE, EYB and Faces94 datasets.

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Data availability

In the proposed method the publically available datasets are used for the performance evaluation whose references are included in the reference section.

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Authors and Affiliations

  1. CSE Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, India

    Shekhar Karanwal & Manoj Diwakar

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  1. Shekhar Karanwal
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  2. Manoj Diwakar
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Correspondence to Shekhar Karanwal.

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The proposed work don’t receive any funds from respective funding organization. The authors don’t have any conflict of interest. This work does not involve humans or animals for experiments evaluation. The experiments are performed on publically available datasets whose references are provided in the reference section.

The authors declare that they have no conflict of interest or competing interests.

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Karanwal, S., Diwakar, M. Triangle and orthogonal local binary pattern for face recognition. Multimed Tools Appl 82, 36179–36205 (2023). https://doi.org/10.1007/s11042-023-15072-y

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