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Not on the face alone: perception of contextualized face expressions in Huntington's disease.
The first approach includes studies intended to predict emotions using face expressions and/or speech signals [10].
Six face expressions were examined using motion-based matchers, Simple Spare Displacement Algorithm (SDA) and Dense Displacement Algorithm (DDA).
However, the main disadvantage of this approach is that permanently catching the spontaneous face expressions that do not reflect real emotions is quite difficult.
Two popular face datasets are selected to evaluate the experimental discretization performance in this section: This employed dataset is a subset of the FERET face dataset [29], in which the images were collected under varying illumination conditions and face expressions.
To highlight the performances of the proposed system, we have carried out the first experiment on the Yale database [23], with the aim of pinpointing the behavior of our approach under changing face expressions and poses.
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On the other hand, fb subset includes frontal face images with alternative face expression.
Using a data-glove the user could control the position and face expression of an animated virtual puppet.
Some sample images from the MMI database are shown in Figure8. Figure 8 The sample face expression images from the MMI database.
just as they can be found for face identity [46] and face expression [47], [48].
First, we examined brain responses to each feedback type, regardless of the concomitant face expression.
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