  What qualities make a feature useful for biometric performance? In prior
research, pre-dating the advent of deep learning (DL) approaches to biometric
analysis, a strong relationship between temporal persistence, as indexed by the
intraclass correlation coefficient (ICC), and biometric performance (Equal
Error Rate, EER) was noted. More generally, the claim was made that good
biometric performance resulted from a relatively large set of weakly
intercorrelated features with high ICC. The present study aimed to determine
whether the same relationships are found in a state-of-the-art DL-based eye
movement biometric system (``Eye-Know-You-Too''), as applied to two publicly
available eye movement datasets. To this end, we manipulate various aspects of
eye-tracking signal quality, which produces variation in biometric performance,
and relate that performance to the temporal persistence and intercorrelation of
the resulting embeddings. Data quality indices were related to EER with either
linear or logarithmic fits, and the resulting model R^2 was noted. As a general
matter, we found that temporal persistence was an important predictor of
DL-based biometric performance, and also that DL-learned embeddings were
generally weakly intercorrelated.
