This whitepaper examines modern machine learning techniques used for face detection in images and video. It explains how traditional rule-based approaches struggle with variations in lighting, pose, occlusion, and image quality, and how deep learning models overcome these limitations. The paper reviews convolutional neural network architectures commonly used for face detection, including multi-stage and single-shot detectors. It also discusses training requirements, labeled data considerations, and performance tradeoffs between accuracy and speed. Use cases such as security, access control, surveillance, and identity verification illustrate how robust face detection systems enable downstream tasks like recognition, tracking, and analytics at scale.

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