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When machines could see you
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In greyscale front-facing images (assuming lighting is balanced), you’ll notice key patterns: the eye-brow region tends to be darker at the top (brows) and lighter below (eyes), while the nose bridge appears brighter compared to the adjacent cheeks. This method allowed for early real-time face detection on low-power devices, but it lacked the holistic capacity seen in the FFA, making it prone to errors when encountering complex angles or lighting conditions that humans would easily overcome. This transition from manually designed features to data-driven learning revolutionized computer vision, allowing machines to not only detect but also understand faces with near-human precision, paving the way for modern facial recognition systems powered by deep neural networks.
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