Application of Preprocessing Techniques in Facial Recognition
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901139560071168 |
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| author | K. Minney Prisilla Dr. N. Jayashri Dr. A. S. Aneeshkumar |
| author_facet | K. Minney Prisilla Dr. N. Jayashri Dr. A. S. Aneeshkumar |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19753821 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Application of Preprocessing Techniques in Facial Recognition K. Minney Prisilla Dr. N. Jayashri Dr. A. S. Aneeshkumar Face recognition, Noise Reduction, Gaussian Filter, Amalgam Denoising Algorithm, Empirical Mode Decomposition; Intrinsic Mode Functions, Local Binary Pattern, Convolutional Neural Network. <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> |
| title | Application of Preprocessing Techniques in Facial Recognition |
| topic | Face recognition, Noise Reduction, Gaussian Filter, Amalgam Denoising Algorithm, Empirical Mode Decomposition; Intrinsic Mode Functions, Local Binary Pattern, Convolutional Neural Network. |
| url | https://doi.org/10.5281/zenodo.19753821 |