Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
This work addresses pain intensity estimation from facial images, which is important for healthcare monitoring, but it appears incremental as it builds on existing ordinal regression frameworks.
The paper tackles the problem of automatic pain intensity estimation from facial images by proposing a novel method based on kernel Conditional Ordinal Random Fields, extended to account for heteroscedasticity and incorporating dynamic features for temporal constraints. The results show that the approach outperforms state-of-the-art methods, with significant improvements in Intra-Class Correlation measure.
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.