  We propose a novel method for automatic pain intensity estimation from facial
images based on the framework of kernel Conditional Ordinal Random Fields
(KCORF). We extend this framework to account for heteroscedasticity on the
output labels(i.e., pain intensity scores) and introduce a novel dynamic
features, dynamic ranks, that impose temporal ordinal constraints on the static
ranks (i.e., intensity scores). Our experimental results show that the proposed
approach outperforms state-of-the art methods for sequence classification with
ordinal data and other ordinal regression models. The approach performs
significantly better than other models in terms of Intra-Class Correlation
measure, which is the most accepted evaluation measure in the tasks of facial
behaviour intensity estimation.
