Facial expression recognition based on Local Binary Patterns: A comprehensive study

By Caifeng Shan, Shaogang Gong, Peter W. McOwan. Image and Vision Computing, 2009

2,364 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bd8d579715d58405dfd5a77f32920aafe018fce4

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Learning active facial patches for expression analysis (2012, positive): “Appearancebased methods[13] [25] [16] reveal the differences among expressions by facial appearance variations, which has been proved to be more reliable on single images.”
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, methodology): “For each frame in the video segment, we run face detection, estimate the eye distance, and ﬁnally crop a RGB face image of size 150 × 110 × 3, as done in [55] and [56].”
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2019, methodology): “The texture-based features mainly include SIFT [45], HOG [13], Histograms of LBP [48], Gabor wavelet coefﬁcients [36], etc.”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, methodology): “[44] MMI - Different SVM kernels trained with LBP features - Subject-independent and cross-database experiments - 86.”
Local Transform Features and Hybridization for Accurate Face and Human Detection (2013, background): “Index Terms—Local binary pattern, local gradient pattern, binary histograms of oriented gradients, feature hybridization, face and human detection Ç”
Recurrent Neural Networks for Emotion Recognition in Video (2015, background): “Traditional approaches to emotion recognition were based on hand-engineered features [17, 28].”
Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition (2014, methodology): “HOE [27] 54.15 LBP-TOP [34] 59.51 HOG 3D [14] 60.89 3D SIFT [23] 64.39 ADL [24] 47.78 HMM [30] 51.5 ITBN [30] 59.7 CPL [35] 49.36 CSPL [35] 73.53 Ours STM 65.37 Ours STM-ExpLet 75.12 Table 2.”
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “They provide valuable information that influences our basic human processes in a bidirectional manner [67, 68].”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, neutral): “Accuracy CSPL [90] 89.9% LBPSVM[62] 95.1% BDBN [41] 96.7% PPDN [89] 97.3% Zero-bias CNN [29] 98.3% Our Method 98.9% six prototypical expressions, i.e. , angry, happy, surprise, sad, disgust, fear, or a non-standard expression ( i.e. contempt).”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, background): “Recently, some studies try to capture the dynamic variation of facial physical structure from consecutive frames based on hand-crafted features or deep learning methods, such as LBP-TOP [4], HOG 3D [5], STM-ExpLet [6], and DTAGN [7].”
Going deeper in facial expression recognition using deep neural networks (2015, background): “These features can be geometric features such as facial landmarks [19], appearance features such as pixel intensities [32], Gabor filters [23], Local Binary Patterns (LBP) [38], Local Phase Quantization (LPQ) [53], and Histogram of Oriented Gradients (HoG) [29], or motion features such as optical fl”
Local Directional Number Pattern for Face Analysis: Face and Expression Recognition (2013, methodology): “Although these methods have been studied widely, local descriptors have gained attention because of their robustness to illumination and pose variations.”
LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition (2014, background): “Local Binary Patterns (LBP) is widely used in facial expression recognition [8] due to its ability to derive local statistical patterns that exhibit invariance towards illumination changes and simplicity in computation.”
Suppressing Uncertainties for Large-Scale Facial Expression Recognition (2020, methodology): “The texture-based features mainly include SIFT [34], HOG [6], His-tograms of LBP [35], Gabor wavelet coefﬁcients [26], etc.”