Image Processing A Decade-by-Decade Review with a Focus on Face Recognition | ||
| SVU-International Journal of Engineering Sciences and Applications | ||
| Volume 6, Issue 2, December 2025, Pages 29-46 PDF (1.2 M) | ||
| Document Type: Reviews Articles. | ||
| DOI: 10.21608/svusrc.2025.339279.1251 | ||
| Authors | ||
| mahmoud hardan fahim* 1; A. A. DONKOL1; Adel Bedair1, 2; Mohamed Abdel-Nasser3 | ||
| 1Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt | ||
| 2School of Electronics, Communications and Computer Engineering, E-JUST, Egypt | ||
| 3Faculty of Engineering, Aswan University, Aswan, Egypt | ||
| Abstract | ||
| The evolution of image processing has been remarkable, transitioning from its initial uses in military and medical fields to its widespread integration in modern society. This article delves into the historical progression of image processing, tracing its growth from the mid-20th century to the current era. It explores crucial milestones, such as the inception of digital computers, the emergence of AI and machine learning, the introduction of deep learning, and the developments following the spread of COVID-19. These advancements have propelled image processing to unprecedented levels, facilitating applications like facial recognition, autonomous driving, and medical image analysis. The paper further scrutinizes the specific evolution of face recognition technology, a prominent facet of image processing. It discusses significant techniques, ranging from early methodologies like Eigenfaces and Fisherfaces to more cutting-edge deep learning-driven approaches. These methodologies have substantially enhanced the accuracy and resilience of face recognition, especially in complex scenarios involving diverse lighting conditions, occlusions, and pose variations. By comprehending the historical backdrop and technological progressions in image processing and face recognition, we can acknowledge the profound influence of these technologies on various sectors and their capacity to mold the future of human-computer interaction. | ||
| Keywords | ||
| image processing; Deep learning; Facial recognition; Image classification; masked Facial recognition | ||
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