Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis

By Mengyi Liu, Shaoxin Li, Shiguang Shan, Ruiping Wang, Xilin Chen. Asian Conference on Computer Vision, 2014

322 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/b6c53891dff24caa1f2e690552a1a5921554f994

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Training deep networks for facial expression recognition with crowd-sourced label distribution (2016, background): “Lately, due to the great success of DCNN in a wide variety of image classification tasks [16, 28], it has also been applied in emotion recognition [14, 18, 17, 29, 33].”
Spatial–Temporal Recurrent Neural Network for Emotion Recognition (2017, methodology): “The method named 3DCNN-DAP proposed in [19] employs 3DCNN while using the strong spatial structural constraints on the dynamic action parts to extract robust representation from videos.”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, methodology): “We also compare with a typical 3DCNN structure proposed by Liu et al. [37].”
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition (2015, methodology): “Additionally, this method has been applied to facial expression recognition with deformable action part constraints, which is called 3D CNNDAP [11].”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019, methodology): “In [42], 3D Convolution-al Neural Networks (3DCNN-DAP) with deformable action parts constraints were adopted to localize the action parts and encode them effectively.”
Facial Expression Recognition by De-expression Residue Learning (2018, background): “Research on facial expression recognition (FER) has been conducted on both posed and spontaneous facial expressions under various imaging conditions, including various head poses, illumination conditions, resolutions, and occlusions [34] [14] [17] [18] [12].”
Going deeper in facial expression recognition using deep neural networks (2015, methodology): “A 3D CNN with deformable action parts constraints is introduced in [25] which can detect specific facial action parts under the structured spatial constraints, and obtain the discriminative part-based representation simultaneously.”
Image based Static Facial Expression Recognition with Multiple Deep Network Learning (2015, background): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, methodology): “Motivated by the domain knowledge that facial expression can be decomposed into a combination of facial action units (AU), a deformable facial part model was explored in [21].”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “[44] adapt 3D Convolutional Neural Network (CNN) to detect specific facial action parts to obtain discriminative part-based representation.”
A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, methodology): “Deep-learning-based FER approaches • Deep learning of deformable facial action parts [69] Sequence 63.”