Journal ArticleDOI
Region-based facial representation for real-time Action Units intensity detection across datasets
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TLDR
A method for the real-time detection of AUs intensity in terms of the Facial Action Coding System scale is proposed, grounded on a novel and robust anatomically based facial representation strategy, for which features are registered from a different region of interest depending on the AU considered.Abstract:
Most research on facial expressions recognition has focused on binary Action Units (AUs) detection, while graded changes in their intensity have rarely been considered.
This paper proposes a method for the real-time detection of AUs intensity in terms of the Facial Action Coding System scale. It is grounded on a novel and robust anatomically based facial representation strategy, for which features are registered from a different region of interest depending on the AU considered. Real-time processing is achieved by combining Histogram of Gradients descriptors with linear kernel Support Vector Machines. Following this method, AU intensity detection models are built and validated through the DISFA database, outperforming previous approaches without real-time capabilities. An in-depth evaluation through three different databases (DISFA, BP4D and UNBC Shoulder-Pain) further demonstrates that the proposed method generalizes well across datasets. This study also brings insights about existing public corpora and their impact on AU intensity prediction.read more
Citations
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Journal ArticleDOI
Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects
TL;DR: Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined.
Journal ArticleDOI
Computational Study of Primitive Emotional Contagion in Dyadic Interactions
TL;DR: A cross-recurrence based methodology that explicitly integrates short and long-term temporal dynamics through the analysis of both facial expressions and sentiment was developed and is able to show the importance of the adoption of cross-modal strategies for addressing emotional contagion.
Book ChapterDOI
Affective facial computing: Generalizability across domains
Jeffrey F. Cohn,Itir Onal Ertugrul,Wen-Sheng Chu,Jeffrey M. Girard,László A. Jeni,Zakia Hammal +5 more
TL;DR: In this paper, the authors evaluated the generalizability of automated facial computing (AFC) systems across domains, and found that cross-domain generalizeability trails that of within-domain performance.
Book ChapterDOI
On the Role of Personality and Empathy in Human-Human, Human-Agent, and Human-Robot Mimicry
TL;DR: This paper explored whether the humanlikeness and embodiment of an agent affect human facial mimicry and which personality and empathy traits are related to facial mimicking of human and artificial agents, and found that mimicry was affected by the agents' embodiment, but not by their human-likeness, and correlated both with individual traits denoting sociability and sympathy.
Journal ArticleDOI
Saliency Maps-Based Convolutional Neural Networks for Facial Expression Recognition
TL;DR: The proposed salient feature extraction model is proposed, including the dilated inception module, the Difference of Gaussian (DOG) module, and the multi-indicator saliency prediction module, which can effectively reflect the key facial information through the increase of the receptive field, the acquisition of multiscale features, andThe simulation of human vision.
References
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Intraclass correlations: uses in assessing rater reliability.
TL;DR: In this article, the authors present guidelines for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges, and the confidence intervals for each of the forms are reviewed.