Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

By Gil Levi, Tal Hassner. International Conference on Multimodal Interaction, 2015

340 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fac82061b74a880b158919d2d06c39ca64a84acf

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering (2016, background): “Over the last few years, a number of valuable datasets and models targeting speciﬁc tasks such as scene recognition [10], age, gender, and emotion classiﬁ-cation [11,12], human action recognition [13,14,15], etc. have been released.”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017, methodology): “Therefore, most deep learning frameworks employed on facial expression recognition [22, 32, 36] are base on pre-trained models.”
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2019, methodology): “Levi and Hassner [33] leverage the CASIA-WebFace[63] face recognition dataset to pretrain four different VGGNet[53] and GoogleNet[55].”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, background): “Recently, although CNN based methods are beginning to dominate, LBP research remains active, as evidenced by signiﬁcant recent work [77, 219, 202, 116, 136, 251, 259, 49].”
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (2017, methodology): “simple [28] 40% Siamese 38% reduced simple 31% reduced Siamese 32%”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, background): “Motivated by this observation, several previous works [8], [9] on expression recognition utilize face recognition datasets to pre-train the network, which is then fine-tuned on the expression dataset.”
Implicit Diversity in Image Summarization (2019, methodology): “For the images without gender labels, we attempt to use pre-trained gender classification tools [21] to auto-label the gender of the images, so as to better evaluate the performance of our algorithm.”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, background): “MBP (Levi and Hassner 2015)—expression recognition with Mapped Binary Patterns (MBP), which is proposed in Levi and Hassner (2015).”
On Face Segmentation, Face Swapping, and Face Perception (2017, background): “…more important than this practice may suggest: Face swapping can also be used for preserving privacy [4, 34, 38], digital forensics [35] and as a potential face speciﬁc data augmentation method [33] especially in applications where training data is scarce (e.g., facial emotion recognition [26]).”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019, methodology): “employed for facial expression recognition [38], [54], [59]”
Deep Facial Expression Recognition: A Survey (2018, background): “A novel mapped LBP feature [79] was proposed for illumination-invariant”
Video Question Answering with Spatio-Temporal Reasoning (2019, background): “Three of them are new and unique to the video domain, which we designed after closely inspecting several previous datasets for video understanding (Soomro et al. 2012; Zhou et al. 2014; Isola et al. 2015; Levi and Hassner 2015).”