Sentence examples for face variability from inspiring English sources

Quantifying face variability is heavily dependent on establishing landmarks across faces.

The proposed model adopts a segment methodology which applies to road segments of at least 3 km [15] or to road segments between two at-grade intersections, so as to face variability of geometric and performance features among road segments.

Variability: Landmark appearances differ due to intrinsic factors such as face variability between individuals, but also due to extrinsic factors such as partial occlusion, illumination, expression, pose and camera resolution.

Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions.

The details of these confounding factors that compromise the performance of facial landmark detection are as follows: Variability: Landmark appearances differ due to intrinsic factors such as face variability between individuals, but also due to extrinsic factors such as partial occlusion, illumination, expression, pose and camera resolution.

In our implementation, we effectively observe a similar increase ( to be precise) when we compare the process time related to the classical AAM, and the one related to our proposition, pre-process step included (Algorithm 2. As previously said in Section 3.3, the AAM robustness is related to the face variability in the learning base.

Even with an act as fundamental, and as frequent, as writing a prescription, doctors face tremendous variability between health care plans.

Together with the description of the innovative aspects of the machine mechanics, designed to face intrinsic variability in vegetable objects, the objective of this paper was to present the ensemble synthesis of the control policies, based on heuristic scheduling priorities, to allocate and coordinate the team of robots to a set of spatially distributed tasks.

Listeners face tremendous variability in the signal, yet demonstrate remarkable ability in identifying the intended speech sound.

In 3D modeling, the face pose variability is transferred from the appearance parameter space to the sub-space which controls the pose (face position and angle).

Since uptake time correction of TLR is currently not applied in clinical routine, one thus actually faces a variability of TLR in comparison to SURtc of 1.14 ± 0.21 (90 % CI 0.82 1.48) if actual scan times are as variable as in our study group.