Sentence examples for face recognition performances from inspiring English sources

Despite the number of researchers and the proposed methods, several factors can significantly affect face recognition performances, such as the pose, the presence/absence of structural components, facial expressions, occlusion, and illumination variations.

Testing for group differences in face recognition performance, we found that CPs, with a median error rate of 14.7%, perform significantly worse than controls, with a median of 6.3% (Wilcoxon rank sum test, W = 267, n0 = 25, nCP = 13, p<0.001 one-sided).

Face registration: Face registration is the single most important factor affecting face recognition performance [24].

The main purpose of SHR is to improve the face recognition performance, particularly at low-resolutions.

However, thermal face recognition performance is still insufficient for practical applications.

Phillips et al.[7, 81] show that simple image quality metrics can be combined to predict face recognition performance.

Face recognition performance is evaluated using Architecture-I which treats images as random variables and pixels as outcomes.

The resulting face descriptor based on multiple representative views, which is of compact size, shows reasonable face recognition performance on any view.

The goal of the experiments, therefore, is to demonstrate and quantify the improvement of face recognition performance if SHR is used for face registration.

By implementing the same type of experiments on these two groups separately, the face recognition performance on non-twins is measured.

Multiband eigenface methods in which the multiple bands are processed independently are shown to improve face recognition performance relative to single-band results.