Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

By Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

366 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/56f14c2d25706c93150d2af79fbe21a663d1d433

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Citations in the corpus

From Facial Expression Recognition to Interpersonal Relation Prediction (2016, background): “Social signal processing mainly involves facial expression recognition [96,70,45,64,80,16,46,44,31,52,16,97].”
Facial Expression Recognition by De-expression Residue Learning (2018, methodology): “Our DeRL method achieves the highest accuracy compared to those of state-of-the-art methods, including CNN-based methods (DTAGN-Joint [12], FN2EN [6], and PPDN [35]) and handcrafted features based methods (LBP-TOP [34], HOG 3D [14] and STM-Explet [18]).”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, methodology): “As in other works [20], we extract the last three frames and the first frame of each video to compose the image-based CK+ database.”
Spatial–Temporal Recurrent Neural Network for Emotion Recognition (2017, methodology): “D SIFT [17], expressionlet-based spatio-temporal manifold (STM-ExpLet) [18], and so on.”
A Riemannian Network for SPD Matrix Learning (2016, methodology): “As show in Table.1, we report the performances of the competing methods including the state-of-the-art method (STM-ExpLet (Liu et al. 2014)) on this database.”
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition (2017, methodology): “As the ground truth of test set is still unreleased, here we only report our results on the validation set for comparison with [11, 14, 25].”
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition (2020, neutral): “Methods BP4D CK+ MMI EmotioNet STM-Explet[27] - 94.”
Building Deep Networks on Grassmann Manifolds (2016, methodology): “General manifold learning methods: Expressionlets on Spatio-Temporal Manifold (STMExpLet) (Liu et al. 2014a) and Riemannian Sparse Representaion combining with Manifold Learning on the manifold of SPD matrices (RSR-SPDML) (Harandi, Salzmann, and Hartley 2014); 2).”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, methodology): “In this paper, we conduct our experiments on all of the 205 sequences with 10-fold cross-validation, which is the same as [6], [7], and [45].”
Image based Static Facial Expression Recognition with Multiple Deep Network Learning (2015, background): “…of related fields, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “In [146], spatio-temporal manifolds of low level features are modelled, [264] uses a maximum of a bag of words (BoW) pyramid over the whole sequence, [147] captures spatio-temporal information through autoen-”
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition (2015, methodology): “Recently, expressionlet-based spatio-temporal manifold representation was developed (STM-ExpLet) [12].”