Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions

Md Manjurul Ahsan,Md Tanvir Ahad,Kishor Datta Gupta,Yueqing Li,Jing Zhang

doi:10.3390/technologies9020031

Md Manjurul Ahsan, Md Tanvir Ahad + Show 3 more

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https://doi.org/10.3390/technologies9020031

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Abstract

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

Highlights

IntroductionFacial recognition (FR) is one of the most widely researched fields and has been improved tremendously in the past few decades [1]
A new face dataset (LUDB) is presented containing images captured in different weathers
The experimental results show that facial recognition (FR) in an unconstrained situation using Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH) is challenging but possible

Summary

Introduction

Facial recognition (FR) is one of the most widely researched fields and has been improved tremendously in the past few decades [1]. Its application has been seen from security systems to the entertainment world. A lot of organizations within the commercial sector, such as mobile companies, software developers, and entertainment companies are developing and launching FR applications for various purposes. FR techniques are still challenged by multiple factors such as low light conditions, facial expressions, and bad weather [2]. The lighting/illumination adjustment is one of the most complex and demanding problems in access control applications based on human face recognition [3]. Identifying a face in a crowd, on the other hand, raises significant concerns about individual liberties and ethical problems as well [4].

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