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It is difficult to identify criminals using biometric verification; high precision is needed to improve public safety and minimize misidentification. The 3D-Local Binary Pattern (3D-LBP) feature descriptor is widely employed in biometric techniques such as fingerprint identification, iris recognition, and facial recognition. A straightforward and effective technique for characterizing local picture content with significant discriminative power is 3D-LBP. In order to extract intricate spatial and temporal information from facial photos, this study uses 3D-LBP to investigate pixel interactions in a three-dimensional framework. The Message Passing Interface (MPI) is used for distributed computing to increase the performance of facial recognition. MPI makes face recognition and feature extraction real-time possible, making it possible to distribute computing work among several processors. Using MPI results in noticeable speedups of around five times, showcasing important developments in face recognition technology that are necessary for contemporary security systems. This make the study to enhance the utilization of the computing power of the CPU so that it can be used in the security systems which now are in high demand.

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Parallelization of 3D-LBP Feature Extraction Using MPI Programming

  • 会议论文

  • 发表日期：

    2026年

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  • Vishwas Prabhu,
  • Nithanth Marate,
  • Rao B. Ashwath,
  • N. Gopalakrishna Kini

摘要

It is difficult to identify criminals using biometric verification; high precision is needed to improve public safety and minimize misidentification. The 3D-Local Binary Pattern (3D-LBP) feature descriptor is widely employed in biometric techniques such as fingerprint identification, iris recognition, and facial recognition. A straightforward and effective technique for characterizing local picture content with significant discriminative power is 3D-LBP. In order to extract intricate spatial and temporal information from facial photos, this study uses 3D-LBP to investigate pixel interactions in a three-dimensional framework. The Message Passing Interface (MPI) is used for distributed computing to increase the performance of facial recognition. MPI makes face recognition and feature extraction real-time possible, making it possible to distribute computing work among several processors. Using MPI results in noticeable speedups of around five times, showcasing important developments in face recognition technology that are necessary for contemporary security systems. This make the study to enhance the utilization of the computing power of the CPU so that it can be used in the security systems which now are in high demand.