Bi-modal emotion recognition from expressive face and body gestures

By Hatice Gunes, Massimo Piccardi. Journal of Network and Computer Applications, 2007

391 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/234320522efc8ef7d15a9ab938d4eddb99c05b2d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

In Human-Computer Interaction

Citations in the corpus

Survey on Emotional Body Gesture Recognition (2018, background): “Historical works usually focused on simple fusion techniques of predefined body and face representations [127], [128].”
Human-Computer Interaction: Overview on State of the Art (2008, neutral): “Another work that integrated face and body modalities is [68] in which the authors showed that, similar to humans, machine classification of emotion is better when based upon face and body data, rather than either modality alone.”
Automatic speech emotion recognition using modulation spectral features (2011, background): “Such speech based systems can function either independently or as modules of more sophisticated techniques that combine other information sources such as facial expression and gesture (Gunes and Piccard, 2007).”
Review on Psychological Stress Detection Using Biosignals (2019, background): “In [117] a combined analysis of facial cues and upper body gestures was performed in recognizing various emotional states including anxiety.”
Affective Body Expression Perception and Recognition: A Survey (2013, methodology): “[122] (6) 4 basic + anxiety, uncertainty 4 actors 27 face videos 27 body videos actor BayesNet 91% & 94%”
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues (2020, background): “For instance, [12, 27] suggests that when different modalities are modeled and projected into a common space, they should point to similar affective cues.”
Social signal processing: Survey of an emerging domain (2009, background): “However, there are few works where the posture is recognized as a social signal, namely to estimate the interest level of children learning to use computers [124], to recognize the affective state of people [38][74] (see [76][221] for exhaustive overview of research efforts in the field), and the in”
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech (2008, background): “Gunes and Piccardi [23] for example fused at different levels facial expressions and body gestures information for bimodal emotion recognition.”
Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2016, background): “Bimodal fusion methods have been proposed in numerous instances [50,51], but optimal information fusion configurations remain elusive.”
Recognising Human Emotions from Body Movement and Gesture Dynamics (2007, background): “Gunes and Piccardi [ 15 ] for example fused facial expression and body gesture information at different levels for bimodal emotion recognition.”
EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, background): “EEG is used to record such activities and is reliable for emotion recognition due to its relatively objective evaluation of emotion compared to nonphysiological clues (facial expression, gesture, etc.) [48, 49].”
Computer Vision and Image Understanding (2016, background): “Increased blinking is associated with increased sympathetic nervous system activity that increases involuntary responses when people are emotionally aroused ( Harrigan and O’Conell, 1996 ), as a result, during anxiety the overall activity of facial muscles increases ( Gunes and Piccardi, 2007 ).”