Deep Learning in Face Recognition
The article surveys deep‑learning‑based face‑recognition systems, detailing detection, preprocessing, and recognition pipelines, describing evaluation metrics such as TAR, FAR, and Rank‑K, reviewing major datasets like LFW, MS‑Celeb‑1M and VGGFace2, and comparing leading architectures—including FaceNet, CenterLoss, SphereFace and InsightFace—while highlighting their strengths, limitations, real‑world applications, and seminal research references.
This article provides a comprehensive overview of face recognition technology, focusing on deep learning approaches. It covers key components like face detection, preprocessing, and recognition stages, along with evaluation metrics (TAR, FAR, Rank-K) and datasets (LFW, MS-Celeb-1M, VGGFace2). The text discusses popular architectures such as FaceNet, CenterLoss, SphereFace, and InsightFace, highlighting their strengths and limitations. Applications in real-world scenarios and references to key research papers are also included.
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