Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity
    Estimation from Facial Images
Authors: Ognjen Rudovic, Maja Pantic, Vladimir Pavlovic
ICLR 2013

Abstract: 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.


Peer review:
[Review]:
This extended abstract discusses a modification to an existing ordinal conditional random field model (CORF) so as to treat non-stationary data. This is done by making the variance in a probit model depend on the observations (x) and appealing to results on kernels methods for CRFs by Lafferty et al. The authors also introduce what they call dynamic ranks, but it is impossible to understand, from this write-up, how these relate to the model. No intuition is provide either. What is the equal sign doing in the definition of dynamic ranks?

Regarding section 2, it is too short and impossible to follow. The authors should rewrite it making sure the mathematical models are specified properly and in full mathematical detail. All the variables and symbols should be defined. I know there are space constraints, but I also believe a better presentation is possible.

The experiments claim great improvements over techniques that do not exploit structure or techniques that exploit structure but which are not suitable for ordinal regression. As it is, it would be impossible to reproduce the results in this abstract. However, it seems that great effort was put into the empirical part of the work.

Some typos:

Abstract: Add space after labels. Also, a novel should be simply novel.
Introduction: in the recent should be in recent. Also, drop the a in a novel dynamic features.
Section 2: Add space after McCullagh. What is standard CRF form? Please be precise as there are many ways of parameterizing and structuring CRFs.
References: laplacian should be Laplacian