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| Autors principals: | , , |
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| Format: | Recurso digital |
| Idioma: | |
| Publicat: |
Zenodo
2025
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| Matèries: | |
| Accés en línia: | https://doi.org/10.5281/zenodo.19753821 |
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- <p>Identifying and recognizing criminals at a crime scene can be a complex and time-consuming<br>process. Criminals may be identified through various methods such as fingerprints, DNA<br>analysis, CCTV footage, or eyewitness testimony. The use of images captured by security<br>cameras, along with fingerprint and DNA matching, requires access to a pre-existing database<br>for effective recognition. Similarly, systems designed for human identification, such as those<br>used for access control or attendance tracking, also rely on image capture and a database for<br>accurate identification.<br>This article discuss the methods for recognizing noised human faces by analyzing their<br>features. Since images are multidimensional and can be affected by external factors that<br>impact their clarity, creating an effective recognition model is a challenging task. To improve<br>the system's accuracy, preprocessing techniques and feature extraction methods are applied to<br>convert images into pixel-based data. The processed data is then fed into a Convolutional<br>Neural Network (CNN) for classification and recognition. The study also examines the<br>impact of three different preprocessing techniques and a comparative study of their<br>effectiveness.</p>