<![CDATA[Modern security systems face complex challenges, especially in accurately and efficiently identifying individuals. Amidst rapid technological advancements, facial recognition systems have emerged as one of the most promising solutions. By leveraging deep learning algorithms, these systems can automatically identify and verify a person's identity from images or videos. However, the challenge lies in making these systems both accurate and fast under various environmental conditions, such as changes in lighting, viewing angles, and facial expressions. This research explores in depth the application of deep learning algorithms, specifically Convolutional Neural Networks (CNNs), in developing facial recognition systems for security applications. We test the performance of current models and analyze the effectiveness, challenges, and ethical implications of this technology. The results show that deep learning significantly improves the accuracy and robustness of facial recognition systems, making it a strong foundation for future security solutions. Nevertheless, issues such as algorithmic bias and high computational requirements remain important areas for further research.]]>
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