6,357 research outputs found
Machine Analysis of Facial Expressions
No abstract
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In this paper, we propose a novel two-stage learning approach for
VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs)
to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels
from face images. The estimated scores are then fed into the personalized
Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by
each person. Personalization of the model is performed using a newly introduced
facial expressiveness score, unique for each person. To the best of our
knowledge, this is the first approach to automatically estimate VAS from face
images. We show the benefits of the proposed personalized over traditional
non-personalized approach on a benchmark dataset for pain analysis from face
images.Comment: Computer Vision and Pattern Recognition Conference, The 1st
International Workshop on Deep Affective Learning and Context Modelin
LOMo: Latent Ordinal Model for Facial Analysis in Videos
We study the problem of facial analysis in videos. We propose a novel weakly
supervised learning method that models the video event (expression, pain etc.)
as a sequence of automatically mined, discriminative sub-events (eg. onset and
offset phase for smile, brow lower and cheek raise for pain). The proposed
model is inspired by the recent works on Multiple Instance Learning and latent
SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in
the videos, approximately. We obtain consistent improvements over relevant
competitive baselines on four challenging and publicly available video based
facial analysis datasets for prediction of expression, clinical pain and intent
in dyadic conversations. In combination with complimentary features, we report
state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR
Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging
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
Context-sensitive dynamic ordinal regression for intensity estimation of facial action units
Modeling intensity of facial action units from spontaneously displayed facial expressions is challenging mainly because of high variability in subject-specific facial expressiveness, head-movements, illumination changes, etc. These factors make the target problem highly context-sensitive. However, existing methods usually ignore this context-sensitivity of the target problem. We propose a novel Conditional Ordinal Random Field (CORF) model for context-sensitive modeling of the facial action unit intensity, where the W5+ (who, when, what, where, why and how) definition of the context is used. While the proposed model is general enough to handle all six context questions, in this paper we focus on the context questions: who (the observed subject), how (the changes in facial expressions), and when (the timing of facial expressions and their intensity). The context questions who and howare modeled by means of the newly introduced context-dependent covariate effects, and the context question when is modeled in terms of temporal correlation between the ordinal outputs, i.e., intensity levels of action units. We also introduce a weighted softmax-margin learning of CRFs from data with skewed distribution of the intensity levels, which is commonly encountered in spontaneous facial data. The proposed model is evaluated on intensity estimation of pain and facial action units using two recently published datasets (UNBC Shoulder Pain and DISFA) of spontaneously displayed facial expressions. Our experiments show that the proposed model performs significantly better on the target tasks compared to the state-of-the-art approaches. Furthermore, compared to traditional learning of CRFs, we show that the proposed weighted learning results in more robust parameter estimation from the imbalanced intensity data
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