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<p><b>Abstract</b>—The research topic of <it>looking at people,</it> that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is “fourth generation” embedded computing: “smart”' environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. This paper will examine the mathematical tools that have proven successful, provide a taxonomy of the problem domain, and then examine the state-of-the-art. Four areas will receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper will discuss some of the research challenges and opportunities.</p>
Looking at people, face recognition, gesture recognition, visual interface, appearance-based vision, wearable computing, ubiquitious.
Alex Pentland, "Looking at People: Sensing for Ubiquitous and Wearable Computing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 107-119, January 2000, doi:10.1109/34.824823
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