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The second flaw, shape variation due to pose changes, can be tackled by considering efficient 3D keypoint detectors.
The eye centers are good candidates as they can be identified with periocular images involving large pose variations, while the other key points suffer from occlusion due to pose changes.
The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes.
We integrate the proposed pose normalization block at different stages of the speech recognition system and quantify the loss of performance related to pose changes and pose normalization techniques.
The main idea of the proposed method lies in the following intuitive observation: partitioning the whole face into multiple patches reduces the face misalignment and variability between different persons and the transformation associated to pose changes can be better approximated with a linear map for the patches than for the whole image.
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Second, to create a dataset of objects using a SwissRanger ToF camera [49] and an electronic device to measure pose changes.
ROI images captured in the different timing include deformation due to hand pose changes.
Therefore, the recognition performance is decreased, since there is nonlinear deformation between ROI images due to hand pose changes.
These studies pointed out the necessity of new visual feature extraction methods robust to illumination and pose changes.
And for uncontrolled hand movement, we set an optimal threshold to ignore huge pose changes in a short time.
It can be seen that Osada's shape functions (D2 and D3) have higher dissimilarity measures compared to KPD2 and KPD3 (see a, b), and shape distributions change as the object is subjected to increase in pose change from master view (c) KPA3 and A3.
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