A real-time automated system for the recognition of human facial expressions

By Keith Anderson, Peter W. McOwan. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2006

358 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bf6216d45606a01b78ba6ff2cf6348e7a43c3d81

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

EEG-based emotion recognition during watching movies (2011, background): “Anderson and McOwan utilized facial expressions to recognize emotion [2].”
Differential entropy feature for EEG-based emotion classification (2013, background): “Many previous studies of emotion recognition are based on non-physiological signals, such as facial-expression-based [2] and voice-based [3] emotion recognition.”
Emotional state classification from EEG data using machine learning approach (2014, neutral): “…EEGデータセットの実験結果から，我々はパワースペクトルの特徴は他の 2種類より優れていることを示す．また，線形動的システムに基づく特徴平滑化方法は感情分類精度を大きく向上させることを示し，感情変化の軌跡はマニホールド学習の学習対象に依存しない特徴量を低下させることで，可視化を可能とした． 5 キーワード Emotion classification, Electroencephalograph, Brain?computer interface, Feature reduction, Support vector machine, Manifold learning 6 参考文献”
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach (2008, methodology): “Thus, the implemented algorithms were tailored toward developing models to recognize the universal expressions from static images or video sequences [3], [4], [8]–[12].”
Facial Expression Analysis (2011, methodology): “In several studies, Gabor wavelet coefficients outperformed optical flow, shape features, and Independent Component Analysis representations [3].”