Modeling naturalistic affective states via facial and vocal expressions recognition

By George Caridakis, Lori Malatesta, Loïc Kessous, Noam Amir, A. Raouzaiou, K. Karpouzis. International Conference on Multimodal Interaction, 2006

943 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b59824006956ee9a6f4dee91e34bd5ec0c900976

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In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Developing crossmodal expression recognition based on a deep neural model (2016, methodology): “…of the problems with methods that use the FACS is the amount of time necessary for extracting and recognizing the expressions, as debated by Sariyanidi, Gunes, and Cavallaro (2015), and the difficulty of representing spontaneous expressions, as discussed by Zeng, Pantic, Roisman, and Huang (2009).”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “Multimodal fusion is one of the original topics in multimodal machine learning, with previous surveys emphasizing early, late and hybrid fusion approaches [52], [255].”
Towards multimodal sentiment analysis: harvesting opinions from the web (2011, methodology): “Also related to our work is the research done on multimodal emotion analysis [7, 23, 32].”
Social signal processing: Survey of an emerging domain (2009, background): “[187]), few studies on discrimination between spontaneous and posed facial gestures like brow actions and smiles based on temporal dynamics of target facial gestures, head and shoulder gestures [195][197], and few studies on multimodal analysis of audio and visual dynamic behaviours for emotion reco”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “Although there is considerable research on the use of facial feature tracking to detect human emotions, a vast majority of this research has focused on the basic emotions instead of the learning-centered states that are of relevance to AutoTutor [Calvo and D Mello 2010; Zeng et al. 2009].”
Facial Expression Recognition via a Boosted Deep Belief Network (2014, background): “Previous Work Extensive efforts have been devoted to recognize facial expressions [19, 31].”
Toward Practical Smile Detection (2009, background): “RECENT years have seen considerable progress in the field of automatic facial expression recognition (see [1], [2], [3], [4] for surveys).”
DISFA: A Spontaneous Facial Action Intensity Database (2013, background): “[5] emphasized the need for publicly available databases of spontaneous facial behavior.”
Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database (2010, methodology): “Automatic detection of emotions has been investigated both from audio, video, and recently also by fusing the audio and video modalities (see (Zeng et al., 2009) for an overview).”
Fully Automatic Recognition of the Temporal Phases of Facial Actions (2012, background): “As mentioned in the introduction, very few works have focused on the problem of spontaneous facial expression recognition so far, and results have been quite limited (see [8] for an overview).”
A Multimodal Database for Affect Recognition and Implicit Tagging (2012, background): “We only review the publicly available spontaneous or naturalistic databases and refer the reader to the following review [2] for posed, audio, and audio-visual databases.”
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, background): “While initial approaches made use of a single modality, more recent approaches opt to use multi-modal data as it has been shown to provide higher recognition accuracy [1]–[3], [12].”
Face Expression Recognition and Analysis: The State of the Art (2012, background): “The interested reader can refer to Zeng et al.’s comprehensive survey [9] to get a complete picture on the recent advances in Affect-Recognition and its applications to HCI.”
3D Constrained Local Model for rigid and non-rigid facial tracking (2012, background): “There have been numerous approaches exploring the tracking and analysis of such facial feature points from single images or image sequences [16, 21, 31].”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, background): “prototypical expressions of emotions [5,4,6,7].”