Facial recognition technology has revolutionized the way we interact with the world around us. From unlocking smartphones to identifying individuals in security footage, facial recognition algorithms have become an integral part of our daily lives. However, with the increasing sophistication of facial recognition technology, it is crucial to critically evaluate its performance and potential implications. This article delves into the analysis of facial recognition algorithms in the Python programming language, exploring their accuracy, efficiency, and broader considerations for responsible implementation. By comparing the performance of three popular algorithms – Eigenfaces, Fisherfaces, and Local Binary Patterns – we aim to identify the algorithm that strikes the most favorable balance between accuracy and efficiency. Furthermore, we discuss the broader implications of facial recognition technology, highlighting the importance of addressing potential biases, ensuring data privacy, and safeguarding individual rights.
| Mualliflar | Qodirov, Ahmadxon, Umarov, Abdumukhtar, Rozaliyev, Abdumalikjon |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2023-12-11 |
| Son | 4 |
| Betlar | 197-205 |
| Til | Rus |
Facial recognition, Python, Local Binary Patterns (LBP), Eigenfaces, Fisherfaces, algorithm
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