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Application of Optimized Local Binary Pattern Algorithm in Small Pose Face Recognition under Machine Vision

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Abstract

With the development of information technology, face recognition technology is becoming more robust against the influence of illumination, pose, and noise. The purpose is to study the application of an optimized Local Binary Pattern (LBP) algorithm based on machine vision in small-pose face recognition, thereby promoting the application of face recognition technologies in various fields. An improved LBP algorithm is proposed, namely the Gradient Local Binary Pattern (GLBP). Multiple path lines are used for sampling, then the neighborhood of two scales in the collected face image is calculated by GLBP, and the two-scale GLBP coding values are fused to obtain Two Gradient Local Binary Pattern (TGLBP) algorithm. Finally, the face recognition performance of the TGLBP algorithm is analyzed under simulation.The recognition accuracy of the proposed system is verified under comparative analysis with different algorithms. The results suggest that the recognition accuracy of the proposed TGLBP algorithm has reached 99.31%, 96.84%, and 95.31%, respectively in background set, expression set, and ornament set, all of which are at least 2.5% higher than that of other algorithms; Under the noise robustness recognition analysis, the robustness recognition accuracy of the proposed TGLBP algorithm model is significantly better than other algorithms, which is always over 90%. Therefore, the proposed TGLBP algorithm has higher face recognition accuracy and better noise robustness. The results can provide an experimental reference for the later intellectual development of image recognition.

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Authors and Affiliations

  1. School of Mechanical Engineering, Xijing University, Xi’an, 710100, China

    Hangzhou Qu & Yinwei Wang

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  1. Hangzhou Qu
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  2. Yinwei Wang
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Correspondence to Hangzhou Qu.

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Qu, H., Wang, Y. Application of Optimized Local Binary Pattern Algorithm in Small Pose Face Recognition under Machine Vision. Multimed Tools Appl 81, 29367–29381 (2022). https://doi.org/10.1007/s11042-021-11809-9

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  • DOI: https://doi.org/10.1007/s11042-021-11809-9

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