Sentence examples for more robust facial from inspiring English sources

More recently, a Local Directional Pattern LDPP) method was introduced for a more robust facial representation [19].

The commensal subspecies are associated with a more robust facial skeleton and a larger beak.

Across subspecies residing in different environments commensal house sparrows have consistently more robust facial craniums and larger, more pointed beaks compared to the non-commensal P. d. bactrianus.

We further found a clear, overall allometric relationship between skull size and shape, larger skulls having a relatively smaller brain case and a larger, more robust facial cranium than smaller ones.

Relative to the size allometry commensal house sparrows exhibit a skull shape consistent with accelerated development (heterochrony), resulting in a more robust facial cranium and a larger, more pointed beak.

In Figure 8b, while the performances of STASM, FFPD, and BORMAN are significantly dropped, AAM and ZhuRamanan are much more robust under different facial expressions.

In conclusion, our results are consistent with the hypothesis that adaptations to food sources provided by human societies, and particularly cultivated cereals, have shaped the beak and skull of commensal house sparrows to become more robust and the facial cranium and beak to become elongated and more pointy.

This can be attributed to the fact that the proposed proCMF is more robust to extract discriminant facial features and has potential superior for FER. Figure 4 gives the reconstructed images of the proposed proCMF, NMF, and proNMF.

Further work is needed to develop more robust, objective measures of facial adiposity.

Motivated by human cognitive processes [12], a more robust way to estimate a facial age is arguably to be in a comparative manner, i.e., learning from a number of comparative relations (a given face is younger or older than another face of known age).

It aims to handle large data in the output of deep learning by using SR, moreover, to overcome local extrema and gradient diffusion problems in the training process, the overall network weights are fine-tuned to reach the global optimum, which makes the entire depth of the neural network more robust, thereby enhancing the performance of facial emotion recognition.