Facial expression recognition from near-infrared videos

By Guoying Zhao, Xiaohua Huang, Matti Taini, S. Li, Matti Pietikäinen. Image and Vision Computing, 2011

748 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0424b1d630e042c6d3e0e29f88589e15bbd76e1c

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, methodology): “First, LSTM models are straightforward in terms”
Attention mechanism-based CNN for facial expression recognition (2020, methodology): “The Oulu-CASIA dataset [29] contains 2,880 image sequences which are collected from 80 subjects and each sequence is labeled with six facial expression labels (i.e., anger, disgust, fear, happiness, sadness, and surprise).”
Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods (2015, result): “This is consistent with another study [56] which also reported that LBP feature is suitable for NIR data .”
Suppressing Uncertainties for Large-Scale Facial Expression Recognition (2020, background): “…have made signiﬁcant progress on facial expression recognition (FER) with algorithms and large-scale datasets, where datasets can be collected in lab-∗ oratory or in the wild, such as CK+ [29], MMI [39], Oulu-CASIA [47], SFEW/AFEW [10], FERPlus [4], AffectNet [32], EmotioNet [11], RAF-DB [22], etc.”
Facial Expression Recognition by De-expression Residue Learning (2018, methodology): “The Oulu-CASIA database [33] contains data captured under three different illumination conditions using two types of cameras.”
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition (2015, methodology): “Similar to the Oulu-CASIA database, there are six kinds of emotion labels.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Oulu-CASIA NIR-VIS (Oulu-CASIA) [74] released in 2011, includes 2,880 image sequences”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “1999), Oulu-CASIA (Zhao et al. 2011), MMI (Pantic et al.”
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition (2017, methodology): “Oulu-CASIA database [45] – includes 480 image sequences of 80 subjects taken under normal illumination conditions.”
Deep Facial Expression Recognition: A Survey (2018, background): “The Oulu-CASIA database includes 2,880 image sequences collected from 80 subjects.”
Facial Expression Recognition with Inconsistently Annotated Datasets (2018, background): “Datasets have sprung up for both in-the-lab and in-the-wild facial expressions, such as CK+[20], MMI[28], Oulu-CASIA[33], SFEW/AFEW[7], AffectNet[22], EmotioNet[2], RAF-DB[16], and others.”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, methodology): “Oulu-CASIA has 480 image sequences taken under Dark, Strong, Weak illumination conditions.”
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism (2019, methodology): “Oulu-CASIA dataset contains six prototypic expressions from 80 people between 23 to 58 years old.”
A Spontaneous Micro-expression Database: Inducement, collection and baseline (2013, background): “Oulu-CASIA [17] 80 adults 2880 videos 6 classes Posed”
Facial Expression Recognition With Visual Transformers and Attentional Selective Fusion (2021, methodology): “Majority of these proposed algorithms are implemented on lab-collected datasets, such as CK +[12], MMI[13] and Oulu-CASIA [14].”