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Coarse Head Pose EstimationWe evaluate two coarse pose estimation schemes, based on (1) a probabilistic model approach base on [1] and (2) a neural network approach. We compare the results of the two techniques for varying resolution, head localization accuracy and required pose accuracy using the CMU PIE database.The probabilistic model approach was more robust to head localization accuracy but did not perform as well on very low-resolution head images. The neural network method was able to perform pose estimation even for head images as small as 8X8 pixels. At this resolution, the neural network was able to determine head pan angle class 88% of the time for 9 poses with a step size of 22.5°. For 5 poses with a step size of 45° recognition was 96%. The neural network was also considerably faster running at over 300Hz on a standard PC. Both methods were very sensitive to head tilt. In our initial tests, the PM approach appeared to be more extensible to data acquired under different conditions. We conjecture that there is a tradeoff between model complexity, extensibility and accuracy. In general for head pose estimation and tracking (fine or coarse), there is a consistent tradeoff between complex models i.e., 3D geometric models with elaborate initialization or specialized training sets, accuracy, and lack of extensibility – i.e. to people who do not fit the model, for which initialization is not as good or for individuals or lighting conditions which differ from the training set. The details for the implementation specifications for
resolution and
localization accuracy can be found in paper [2].
Click on the following image to see a demo (video
5.7MB MPEG1). The quadrants are:
[1] Wide-Range Person-
and Illumination-Insensitve
Head Orientation Estimation [2]
Comparative Study of Coarse Head Pose Estimation Other Research Areas:
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