Head direction estimation from low resolution images with scene adaptation
Computer Vision and Image Understanding
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We present an algorithm to estimate the pose of a human head from a single image. This algorithm covers a wide range of head orientations and can be used at various image resolutions. It does not need personalized initialization and is also relatively insensitive to illumination. It works on cropped images obtained by simple head alignment and is fast enough to be used in real time systems. The algorithm builds on the fact that only a limited set of cues are required to estimate human head pose and that most images contain far too many details than what are required for this task. Thus the algorithm abstracts an image using non-photorealistic rendering to reduce the level of detail and to accentuate critical parts. The maximum likelihood pose range is then estimated on a scaled down abstracted image. The proposed algorithm performs very well, while significantly saving on computation.