Denoising face recognition system
| dc.contributor.author | Abdrakhim D. | |
| dc.date.accessioned | 2025-04-01T04:44:20Z | |
| dc.date.available | 2025-04-01T04:44:20Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This thesis investigates the influence of sophisticated denoising techniques on the efficacy of face recognition systems, particularly in environments characterized by substantial image noise. Considering the dependency of face recognition algorithms on the quality of input images, this research conducts a comprehensive evaluation of various denoising strategies, ranging from conventional filters like Median, Bilate-ral, and Gaussian, to advanced deep learning approaches, exemplified by the Deep Convolutional Neural Network (DnCNN). The Extended Yale B dataset, augmented with synthetically introduced noise, provides the basis for this empirical study. Employing quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), coupled with qualitative evaluations, this dissertation quantifies the enhancements in image quality and recognition precision afforded by each denoising method. The findings affirm that the integration of advanced denoising algorithms markedly improves the accuracy of face recognition systems, highlighting the efficacy of adaptive deep learning solutions in addressing the complexities introduced by noisy visual environments. | |
| dc.identifier.citation | Abdrakhim Diana / Denoising face recognition system / 2024 / Computer Science - 7M06102 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1674 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Engineering and Natural Science | |
| dc.title | Denoising face recognition system | |
| dc.type | Other |