2021 Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints
Joseph Agyapong Mensah, Louis Asiedu, Felix O. Mettle, Samuel Iddi
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J. Appl. Math. 2021: 1-12 (2021). DOI: 10.1155/2021/7060270


Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.


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Joseph Agyapong Mensah. Louis Asiedu. Felix O. Mettle. Samuel Iddi. "Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints." J. Appl. Math. 2021 1 - 12, 2021. https://doi.org/10.1155/2021/7060270


Received: 17 June 2021; Revised: 7 August 2021; Accepted: 2 September 2021; Published: 2021
First available in Project Euclid: 28 July 2021

Digital Object Identifier: 10.1155/2021/7060270

Rights: Copyright © 2021 Hindawi


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