Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Asiedu, Louis; Essah, Bernard Obo; Iddi, Samuel; Doku-Amponsah, Kwabena; Mettle, Felix O.

doi:10.1155/2021/5541522

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…From the numerical results, it can be concluded that when varying expressions are the underlying constraints, left reconstructed face images have relatively higher average recognition rate as compared to the right reconstructed face images using the DWT-PCA/SVD recognition algorithm. This finding is consistent with those of Singh and Nandi [11], and Asiedu et al [8,15] with the exception that their study focused on single constraints. The relatively lower average recognition rates of the DWT-PCA/SVD algorithm found in this study reflect the use of multiple constraints (partial occlusion and varying expressions).…”

Section: Conclusion and Recommendationsupporting

confidence: 92%

“…Asiedu et al [15] assessed the performance of Principal Component Analysis and Singular Value Decomposition algorithm using Discrete Wavelet Transform (DWT-PCA/SVD) as a preprocessing mechanism on reconstructed face image database. The reconstruction of the half-face images was done leveraging on the property of bilateral symmetry of frontal faces.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Mensah

Asiedu

Mettle

et al. 2021

Journal of Applied Mathematics

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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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Section: Conclusion and Recommendationsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Mensah

Asiedu

Mettle

et al. 2021

Journal of Applied Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this reason, these methods are also called imprecise methods because random mechanisms have a significant impact on their structure [29]. Among the most important and widely used approaches, we can mention the genetic algorithm [30], differential evolution algorithm [31], particle swarm algorithm [32], ant colony algorithm [33], honey bee algorithm [34], and... which are widely used in various industries today In research [35], a face recognition algorithm based on wavelet transform and compression coefficient value is presented. The classification method is based on sparse representation (SRC) and is done by comparing the residual errors between test samples and reconstruction samples.…”

Section: -Related Workmentioning

confidence: 99%

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

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One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. Based on this, it is expected that the processing methods will be concentrated in this area. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. These images in the recognition system may affect the efficiency of the system, and as a result, it may be inferred that the percentage of recognition is found. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms (genetics and gray wolf) are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. So how arranging the length of the bit string of each block is appropriate to achieve the target estimation to achieve the highest recognition accuracy value, PSNR value, and SSIM value of the image set? In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study, and the recognition efficiency of the images for the conditions without/with compression and the common SPIHT and JPEG coding methods and compared with the proposed method. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM, assuming the highest recognition accuracy can be reached in both meta-heuristic algorithms. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.

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“…The most influential and widely used approaches include genetic algorithms [29], differential evolution algorithms [30], particle swarm algorithms [31], ant colony algorithms [32], honey bee algorithms [33], used in various industries today. Asiedu et al [34] presented a face recognition algorithm based on wavelet transforms and compression coefficient values. Based on sparse representation (SRC), the classification method compares residual errors between test samples and reconstruction samples.…”

Section: Related Workmentioning

confidence: 99%

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

khodadadi

Ardeshir

Grailu

2023

IJE

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In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

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Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Cited by 11 publications

References 9 publications

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

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