Sentence examples for race estimation from inspiring English sources

In the last few years, the race estimation problem has been addressed through several approaches.

In particular, Fig. 12 a refers to gender estimation, whereas Fig. 12 b refers to race estimation.

On the other hand, for race estimation, facial images were split into four categories: White, Black, Asian, and Hispanic.

A compact view of the performance, for gender and race estimation, is presented in Fig. 12 by means of bar diagrams.

Using the same evaluation criteria reported above, Table 2 shows that, for race estimation, the CLBP descriptor, using an unbalanced dataset and non-scaled values as input to the classifier, returned the best total classification accuracy (79.4 %).

LBP was also used in [58] and then fused with Weber local descriptors through concatenation to produce a more powerful set of features to be supplied to a minimum distance classifier for the final race estimation.

In particular, tables report gender, race, and age estimation correctness for all the descriptors and over all the three experimental conditions (frontal-close images I FC, rotated-close images I RC, frontal-far images I FF).

First, some of the most used facial descriptors for gender, race, or age estimation were selected, and then, their performances, by using each of them into the considered framework, were compared.

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study.

The race model allows estimation of the time required to suppress a go response (stop signal reaction time).

As SCr is affected by several factors including age, sex, race, and body size, estimation of GFR using prediction equations is recommended to avoid the misclassification of individuals on the basis of SCr alone [ 14, 15].