| DC Field | Value | Language |
|---|
| dc.contributor.author | Sheronov, I. P. | - |
| dc.contributor.author | Petrov, S. N. | - |
| dc.date.accessioned | 2020-11-23T08:00:19Z | - |
| dc.date.available | 2020-11-23T08:00:19Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.citation | Sheronov, I. P. Opencv-based application for face detection and recognition / Sheronov I. P., Petrov S. N. // Современные средства связи : материалы XХV Междунар. науч.-техн. конф., 22–23 окт. 2020 года, Минск / Белорусская государственная академия связи ; редкол.: А. О. Зеневич [и др.]. – Минск : БГАС, 2020. – С. 223-224. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/41190 | - |
| dc.description.abstract | Face recognition is the automatic localization of a human face in an image or video and, if necessary,
identification of a person's identity based on available databases. Interest in these systems is very high due to
the wide range of tasks that they solve. To build our OpenCV face recognition pipeline, we’ll be applying deep learning in two key steps: to apply face detection, which detects the presence and location of a face in an image, but does not identify it; to extract the 128-d feature vectors (so called embeddings) that quantify each face in an image. For working with data arrays, the Python programming language is used, including the scikit-learn library for extracting features from a data set. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | Белорусская государственная академия связи | ru_RU |
| dc.subject | публикации ученых | ru_RU |
| dc.subject | face recognition | ru_RU |
| dc.subject | OpenCV | ru_RU |
| dc.subject | person's verification | ru_RU |
| dc.title | Opencv-based application for face detection and recognition | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Публикации в изданиях Республики Беларусь
|
