2021
DOI: 10.1155/2021/4981394
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Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

Abstract: The drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environm… Show more

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Cited by 5 publications
(3 citation statements)
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References 26 publications
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“…Transform domain denoising of noisy images involves a forward transformation using specified basis functions, a denoising process and an inverse transformation. The forward transformation process renders an output image showing the different frequency components of the noisy image or images showing different resolution form of an input image as in Discrete Wavelet Transforms [32] . [33] posited that the DWT presents a fast and easy way to filter facial images.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Transform domain denoising of noisy images involves a forward transformation using specified basis functions, a denoising process and an inverse transformation. The forward transformation process renders an output image showing the different frequency components of the noisy image or images showing different resolution form of an input image as in Discrete Wavelet Transforms [32] . [33] posited that the DWT presents a fast and easy way to filter facial images.…”
Section: Methodsmentioning
confidence: 99%
“…Many researchers [42] , [32] have employed MICE augmentation to reconstruct occluded face images, and recommended MICE as a suitable imputation technique for finding missing pixel values in occluded images.…”
Section: Methodsmentioning
confidence: 99%
“…Finally, the CNN model has been fine-tuned by trading off data augmentation and deep learning features. The effectiveness of data augmentation in occluded faces is proposed in Asiedu et al (2021) .…”
Section: Literature Surveymentioning
confidence: 99%