Effective preprocessing techniques for improved facial recognition under variable conditions
Facial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ul...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000155 |
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| author | Afolabi I. Awodeyi Omolegho A. Ibok Idama Omokaro Jones U. Ekwemuka Michael O. Ighofiomoni |
| author_facet | Afolabi I. Awodeyi Omolegho A. Ibok Idama Omokaro Jones U. Ekwemuka Michael O. Ighofiomoni |
| author_sort | Afolabi I. Awodeyi |
| collection | DOAJ |
| description | Facial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ultimately boosting recognition accuracy. This study evaluates advanced preprocessing methods, including edge detection using the Canny detector and illumination normalization through histogram equalization and gamma correction, which are integrated into a preprocessing pipeline. A detailed comparative analysis demonstrates significant recognition rate improvements under low-light and occluded scenarios, supported by quantitative evidence. Additionally, computational efficiency is evaluated, highlighting the applicability of these methods for large-scale and real-time systems. The results affirm that effective preprocessing strengthens the performance and reliability of facial recognition systems, making them suitable for real-world applications where conditions are often unpredictable. |
| format | Article |
| id | doaj-art-eeebda65ec934e3397007ef6add15cd5 |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-eeebda65ec934e3397007ef6add15cd52025-02-10T04:35:33ZengElsevierFranklin Open2773-18632025-03-0110100225Effective preprocessing techniques for improved facial recognition under variable conditionsAfolabi I. Awodeyi0Omolegho A. Ibok1Idama Omokaro2Jones U. Ekwemuka3Michael O. Ighofiomoni4Corresponding author.; Department of Computer Engineering, Delta State University of Science and Technology, Ozoro NigeriaDepartment of Computer Engineering, Delta State University of Science and Technology, Ozoro NigeriaDepartment of Computer Engineering, Delta State University of Science and Technology, Ozoro NigeriaDepartment of Computer Engineering, Delta State University of Science and Technology, Ozoro NigeriaDepartment of Computer Engineering, Delta State University of Science and Technology, Ozoro NigeriaFacial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ultimately boosting recognition accuracy. This study evaluates advanced preprocessing methods, including edge detection using the Canny detector and illumination normalization through histogram equalization and gamma correction, which are integrated into a preprocessing pipeline. A detailed comparative analysis demonstrates significant recognition rate improvements under low-light and occluded scenarios, supported by quantitative evidence. Additionally, computational efficiency is evaluated, highlighting the applicability of these methods for large-scale and real-time systems. The results affirm that effective preprocessing strengthens the performance and reliability of facial recognition systems, making them suitable for real-world applications where conditions are often unpredictable.http://www.sciencedirect.com/science/article/pii/S2773186325000155Facial recognitionPreprocessing techniquesEdge detectionImage normalizationCanny DetectorHistogram Equalization |
| spellingShingle | Afolabi I. Awodeyi Omolegho A. Ibok Idama Omokaro Jones U. Ekwemuka Michael O. Ighofiomoni Effective preprocessing techniques for improved facial recognition under variable conditions Franklin Open Facial recognition Preprocessing techniques Edge detection Image normalization Canny Detector Histogram Equalization |
| title | Effective preprocessing techniques for improved facial recognition under variable conditions |
| title_full | Effective preprocessing techniques for improved facial recognition under variable conditions |
| title_fullStr | Effective preprocessing techniques for improved facial recognition under variable conditions |
| title_full_unstemmed | Effective preprocessing techniques for improved facial recognition under variable conditions |
| title_short | Effective preprocessing techniques for improved facial recognition under variable conditions |
| title_sort | effective preprocessing techniques for improved facial recognition under variable conditions |
| topic | Facial recognition Preprocessing techniques Edge detection Image normalization Canny Detector Histogram Equalization |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000155 |
| work_keys_str_mv | AT afolabiiawodeyi effectivepreprocessingtechniquesforimprovedfacialrecognitionundervariableconditions AT omoleghoaibok effectivepreprocessingtechniquesforimprovedfacialrecognitionundervariableconditions AT idamaomokaro effectivepreprocessingtechniquesforimprovedfacialrecognitionundervariableconditions AT jonesuekwemuka effectivepreprocessingtechniquesforimprovedfacialrecognitionundervariableconditions AT michaeloighofiomoni effectivepreprocessingtechniquesforimprovedfacialrecognitionundervariableconditions |