1,109 research outputs found
A Spatio-Temporal Probabilistic Framework for Dividing and Predicting Facial Action Units
This thesis proposed a probabilistic approach to divide the Facial Action Units (AUs) based on the physiological relations and their strengths among the facial muscle groups. The physiological relations and their strengths were captured using a Static Bayesian Network (SBN) from given databases. A data driven spatio-temporal probabilistic scoring function was introduced to divide the AUs into : (i) frequently occurred and strongly connected AUs (FSAUs) and (ii) infrequently occurred and weakly connected AUs (IWAUs). In addition, a Dynamic Bayesian Network (DBN) based predictive mechanism was implemented to predict the IWAUs from FSAUs. The combined spatio-temporal modeling enabled a framework to predict a full set of AUs in real-time. Empirical analyses were performed to illustrate the efficacy and utility of the proposed approach. Four different datasets of varying degrees of complexity and diversity were used for performance validation and perturbation analysis. Empirical results suggest that the IWAUs can be robustly predicted from the FSAUs in real-time and was found to be robust against noise
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Over the past few years, Convolutional Neural Networks (CNNs) have shown
promise on facial expression recognition. However, the performance degrades
dramatically under real-world settings due to variations introduced by subtle
facial appearance changes, head pose variations, illumination changes, and
occlusions.
In this paper, a novel island loss is proposed to enhance the discriminative
power of the deeply learned features. Specifically, the IL is designed to
reduce the intra-class variations while enlarging the inter-class differences
simultaneously. Experimental results on four benchmark expression databases
have demonstrated that the CNN with the proposed island loss (IL-CNN)
outperforms the baseline CNN models with either traditional softmax loss or the
center loss and achieves comparable or better performance compared with the
state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure
Spatio-Temporal Relation and Attention Learning for Facial Action Unit Detection
Spatio-temporal relations among facial action units (AUs) convey significant
information for AU detection yet have not been thoroughly exploited. The main
reasons are the limited capability of current AU detection works in
simultaneously learning spatial and temporal relations, and the lack of precise
localization information for AU feature learning. To tackle these limitations,
we propose a novel spatio-temporal relation and attention learning framework
for AU detection. Specifically, we introduce a spatio-temporal graph
convolutional network to capture both spatial and temporal relations from
dynamic AUs, in which the AU relations are formulated as a spatio-temporal
graph with adaptively learned instead of predefined edge weights. Moreover, the
learning of spatio-temporal relations among AUs requires individual AU
features. Considering the dynamism and shape irregularity of AUs, we propose an
attention regularization method to adaptively learn regional attentions that
capture highly relevant regions and suppress irrelevant regions so as to
extract a complete feature for each AU. Extensive experiments show that our
approach achieves substantial improvements over the state-of-the-art AU
detection methods on BP4D and especially DISFA benchmarks
Regularizing Face Verification Nets For Pain Intensity Regression
Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain, such as face verification, can alleviate
this problem. In this paper, we propose a network that fine-tunes a
state-of-the-art face verification network using a regularized regression loss
and additional data with expression labels. In this way, the expression
intensity regression task can benefit from the rich feature representations
trained on a huge amount of data for face verification. The proposed
regularized deep regressor is applied to estimate the pain expression intensity
and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving
the state-of-the-art performance. A weighted evaluation metric is also proposed
to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201
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