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Historically, features were extracted using methods like Local Binary Patterns (LBP) or Histograms of Oriented Gradients (HOG). However, contemporary systems rely on Deep Convolutional Neural Networks (DCNNs), such as FaceNet or VGGFace.

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Facial recognition technology (FRT) has evolved from a novel academic pursuit into a ubiquitous component of modern security and social media infrastructure. The ability to detect a human face within a complex visual environment, extract unique features, and link that face to a specific identity involves a multi-stage pipeline. While these technologies offer significant utility in authentication and organization, they raise profound questions regarding privacy, consent, and the ethical handling of biometric data. This paper outlines the core architectures driving these systems and analyzes the implications of their deployment. The ability to detect a human face within