Image based Static Facial Expression Recognition with Multiple Deep Network Learning

By Zhiding Yu, Cha Zhang. International Conference on Multimodal Interaction, 2015

602 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/78af700ce579b3250939c19ed36b58ca0c95077e

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2019, methodology): “[57] ensembles multiple CNNs with each CNN model initialized randomly or pretrained on FER2013.”
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild (2018, methodology): “During training, we follow the data augmentation scheme comprising a ne distortions of the input images introduced in [63] to encourage robustness to variations in pose.”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, methodology): “In [24], [25], a two-step training procedure is suggested, where in the first step, the network was trained using a relatively large expression dataset followed by training on the target dataset.”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019, background): “Note that Kim et al. [33], Ng et al. [54], and Yu and Zhang [77] all trained their models with additional data from SFEW.”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “Following the convention of current studies (Yu and Zhang 2015; Liu et al. 2015; Khorrami et al. 2015; Liu et al. 2014c), we use the overall classification accuracy instead of the balanced accuracy as Eq.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “These pre-processing techniques include face alignment [233], face normalization [234], and pose normalization [235].”
Modeling Stylized Character Expressions via Deep Learning (2016, background): “In the current practice of facial expression analysis, CNNs have shown the capability to learn the features that statistically allow the network to make the correct classification of the input data in various ways [29,30].”
Training deep networks for facial expression recognition with crowd-sourced label distribution (2016, methodology): “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].”
Data Augmentation in Emotion Classification Using Generative Adversarial Networks (2017, neutral): “Similar experiments were reported in [29].”
Deep Facial Expression Recognition: A Survey (2018, background): “loss and hinge loss [53], exponentially weighted average in Fig.”