Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor

Md Manjurul Ahsan,Md Tanvir Ahad,Yueqing Li,Jing Zhang,Munshi Md Shafwat Yazdan

doi:10.3390/jsan9040054

Abstract

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.

Highlights

Facial recognition (FR) has drawn more and more attention among researchers and practitioners over the last few decades due to its extensive use in many security fields
The performance of FR techniques is not satisfactory in many cases due to the following limitations: large dataset led to high computation cost [3]; low light/illuminations caused low FR accuracy [1]; inadequate features of the face produced unstable FR models [4]
Most of the recent drones produce high-resolution images; such large resolutions images with deep learning approaches usually results in a complex and computationally expensive model. Considering those factors, we developed and tested novel algorithms named Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) in this work to develop a fast FR system with limited data

Summary

Introduction

Facial recognition (FR) has drawn more and more attention among researchers and practitioners over the last few decades due to its extensive use in many security fields. The application of deep learning requires a massive amount of data to develop stable FR techniques. Thereby, general algorithms such as Eigenfaces, Fisherfaces, and Local binary pattern histogram (LBPH) are still extensively used to develop FR applications due to the overall suitable and simple performance [1,5,6,7]. LBPH showed robustness in unconstrained conditions like low light and illumination variations, so it has been more widely used than the other two algorithms (Fisherface and Eigenfaces) [8,9]. The overall experiment is carried out on two datasets–5_celebrity (obtained from Kaggle database repository) [10] and the Lamar University data base (LUDB) (contains the facial image of Lamar University students) [1]

Objectives

Results

Discussion

Conclusion