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Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Active Appearance Models Revisited
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Coding facial expressions with Gabor wavelets
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Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2012.6248014openalex-snapshot
Face detection, pose estimation, and landmark localization in the wild
inferredcites →https://doi.org/10.1109/fg.2011.5771366openalex-snapshot
Emotion recognition using PHOG and LPQ features
inferredcites →https://doi.org/10.1109/fg.2013.6553807openalex-snapshot
Facial expression recognition using HessianMKL based multiclass-SVM
inferredcites →https://doi.org/10.1109/icassp.2013.6638346openalex-snapshot
Deep learning for robust feature generation in audiovisual emotion recognition
inferredcites →https://doi.org/10.1109/iccvw.2011.6130508openalex-snapshot
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
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Web-Based Database for Facial Expression Analysis
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2019 IEEE International Conference «Quality Management, Transport and Information Security, Information Technologies»
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Collecting Large, Richly Annotated Facial-Expression Databases from Movies
inferredcites →https://doi.org/10.1109/tpami.2002.1017623openalex-snapshot
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
inferredcites →https://doi.org/10.1109/tpami.2003.1251154openalex-snapshot
The CMU pose, illumination, and expression database
inferredcites →https://doi.org/10.1109/tpami.2007.1110openalex-snapshot
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
inferredcites →https://doi.org/10.1109/wacv.2014.6835735openalex-snapshot
A lp-norm MTMKL framework for simultaneous detection of multiple facial action units
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Partial least squares regression on grassmannian manifold for emotion recognition
inferredcites →https://doi.org/10.1145/2522848.2531739openalex-snapshot
Emotion recognition in the wild challenge 2013
inferredcites →https://doi.org/10.1145/2522848.2531741openalex-snapshot
Multiple kernel learning for emotion recognition in the wild
inferredcites →https://doi.org/10.1145/2522848.2531745openalex-snapshot
Combining modality specific deep neural networks for emotion recognition in video
inferredcites →https://doi.org/10.1145/2663204.2666275openalex-snapshot
Emotion Recognition In The Wild Challenge 2014
inferredcites →https://doi.org/10.1146/annurev.psych.54.101601.145102openalex-snapshot
Facial and Vocal Expressions of Emotion
inferredcites →https://doi.org/10.21437/interspeech.2010-739openalex-snapshot
The INTERSPEECH 2010 paralinguistic challenge
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Feature Detection and Tracking with Constrained Local Models
inferredcites →https://doi.org/10.5555/1953048.2021071openalex-snapshot
Multiple Kernel Learning Algorithms
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Noisy Student Training Using Body Language Dataset Improves Facial Expression Recognition
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Emotion Recognition - A Tool to Improve Meeting Experience for Visually Impaired
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Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels
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