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Automatic pain intensity estimation plays a pivotal role in healthcare and
medical fields. While many methods have been developed to gauge human pain
using behavioral or physiological indicators, facial expressions have emerged
as a prominent tool for this purpose. Nevertheless, the dependence on labeled
data for these techniques often renders them expensive and time-consuming. To
tackle this, we introduce the Adaptive Hierarchical Spatio-temporal Dynamic
Image (AHDI) technique. AHDI encodes spatiotemporal changes in facial videos
into a singular RGB image, permitting the application of simpler 2D deep models
for video representation. Within this framework, we employ a residual network
to derive generalized facial representations. These representations are
optimized for two tasks: estimating pain intensity and differentiating between
genuine and simulated pain expressions. For the former, a regression model is
trained using the extracted representations, while for the latter, a binary
classifier identifies genuine versus feigned pain displays. Testing our method
on two widely-used pain datasets, we observed encouraging results for both
tasks. On the UNBC database, we achieved an MSE of 0.27 outperforming the SOTA
which had an MSE of 0.40. On the BioVid dataset, our model achieved an accuracy
of 89.76%, which is an improvement of 5.37% over the SOTA accuracy. Most
notably, for distinguishing genuine from simulated pain, our accuracy stands at
94.03%, marking a substantial improvement of 8.98%. Our methodology not only
minimizes the need for extensive labeled data but also augments the precision
of pain evaluations, facilitating superior pain management.