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The proliferation of digital media has necessitated the development of advanced systems for facial detection, recognition, and identity linking. This paper explores the technical underpinnings of modern facial recognition pipelines, focusing on the transition from traditional feature extraction to deep learning-based embedding spaces. We examine how unique facial signatures are generated and linked across disparate datasets, culminating in a discussion on the critical privacy concerns and potential for misuse in "deepfake" technologies and unauthorized content distribution.
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