{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:12:15Z","timestamp":1778047935903,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T00:00:00Z","timestamp":1606521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.<\/jats:p>","DOI":"10.3390\/jsan9040054","type":"journal-article","created":{"date-parts":[[2020,11,29]],"date-time":"2020-11-29T21:00:57Z","timestamp":1606683657000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0900-7930","authenticated-orcid":false,"given":"Md Manjurul","family":"Ahsan","sequence":"first","affiliation":[{"name":"Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7422-9631","authenticated-orcid":false,"given":"Yueqing","family":"Li","sequence":"additional","affiliation":[{"name":"Industrial and Systems Engineering, Lamar University, Beaumont, TX 77705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science, Lamar University, Beaumont, TX 77705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-8668","authenticated-orcid":false,"given":"Md Tanvir","family":"Ahad","sequence":"additional","affiliation":[{"name":"School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6067-1453","authenticated-orcid":false,"given":"Munshi Md. Shafwat","family":"Yazdan","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, Idaho State University, Pocatello, ID 83209, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"ref_1","unstructured":"Ahsan, M.M. (2018). Real Time Face Recognition in Unconstrained Environment, Lamar University-Beaumont."},{"key":"ref_2","unstructured":"Fu, Y. (2015). Face Recognition in Uncontrolled Environments. [Ph.D. Thesis, University College London (UCL)]."},{"key":"ref_3","first-page":"59","article-title":"Extending the Storage Capacity And Noise Reduction of a Faster QR-Code","volume":"9","author":"Gupta","year":"2018","journal-title":"Brain Broad Res. Artif. Intell. 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