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 paper seeks to estimate ordinal labels of pain intensity from
videos of faces.  The paper discusses a new variation of a conditional
random field in which the produced labels are ordinal values.  The
paper's main claim to novelty is the idea of 'dynamic ranks', but it
is unclear what these are.

This paper does not convey its ideas clearly.  It is not immediately
obvious why an ordinal regression problem demands a CRF, much less a
kernelized heteroscedastic CRF.  Since I assume that each frame has a
single label, is the function of the CRF simply to impose temporal
smoothness constraints?  I don't understand the motivation for the
additional aspects of this.  The idea of 'dynamic ranks' is not
explained, beyond Equation (2), which is itself confusing.  For
example, what does the equal sign inside the parentheses mean on the
left side of Equation 2?  It took me quite a while of looking at the
right-hand side of this equation to realize that it was defining a
set, but I don't understand how this relates to ranking or dynamics.
Section 3 seems to imply that the kernel is between the features of
6x6 patches, but this doesn't make sense to me if the objective is to
have temporal smoothing.

I found this paper very confusing.  It does not provide many details
or intuition.  In trying to resolve this confusion, I examined the
authors' previous work, cited as [14] and [15].  These other papers
appear to contain most of the crucial details and assumptions that sit
behind the present paper.  I appreciate that this is a very short
paper, but for it to be a useful contribution it must be at least
somewhat self contained.  As it stands, I do not feel this is
achieved.