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

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Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA
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Industrial and Systems Engineering, Lamar University, Beaumont, TX 77705, USA
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Computer Science, Lamar University, Beaumont, TX 77705, USA
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School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
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Computer Science Department, University of Memphis, Memphis, TN 38152, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Manoj Gupta
Received: 14 March 2021 / Revised: 12 April 2021 / Accepted: 20 April 2021 / Published: 27 April 2021
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. View Full-Text
Keywords: face recognition; unconstrained weather; time complexity; accuracy face recognition; unconstrained weather; time complexity; accuracy
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MDPI and ACS Style

Ahsan, M.M.; Li, Y.; Zhang, J.; Ahad, M.T.; Gupta, K.D. Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions. Technologies 2021, 9, 31. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9020031

AMA Style

Ahsan MM, Li Y, Zhang J, Ahad MT, Gupta KD. Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions. Technologies. 2021; 9(2):31. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9020031

Chicago/Turabian Style

Ahsan, Md M., Yueqing Li, Jing Zhang, Md T. Ahad, and Kishor D. Gupta 2021. "Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions" Technologies 9, no. 2: 31. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9020031

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