Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Müller, Lea; Shadaydeh, Maha; Thümmel, Martin; Kessler, Thomas M.; Schneider, Dana; Denzler, Joachim

doi:10.48550/arxiv.1810.12171

Cited by 2 publications

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“…Indeed, in real world cases, dependencies among time series are usually nonlinear and ignoring such interactions could lead to inconsistent estimation. However, using causal knowledge to improve machine learning algorithms remains an open area [ 98 , 100 ], and causal analysis in affective computing is at best in its infancy apart from a handful of exceptions (e.g., [ 100 , 101 , 102 , 103 , 104 , 105 ]).…”

Section: Discussionmentioning

confidence: 99%

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

D’Amelio

Patania

Buršić

et al. 2023

Sensors

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A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

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Section: Discussionmentioning

confidence: 99%

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

D’Amelio

Patania

Buršić

et al. 2023

Sensors

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“…Causal inference is also being considered in recent facial expression studies [18,24]. Therefore, causal learning should also be considered in deep learning based facial expression recognition studies.…”

Section: Causality Learningmentioning

confidence: 99%

Causal affect prediction model using a facial image sequence

Oh,

Jeong,

Lim

2021

Preprint

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Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. However, for improved performance, not only past images but also future images should be used along with corresponding facial images, but there are obstacles to the application of this technique to real-time environments. In this paper, we propose the causal affect prediction network (CAPNet), which uses only past facial images to predict corresponding affective valence and arousal. We train CAPNet to learn causal inference between past images and corresponding affective valence and arousal through supervised learning by pairing the sequence of past images with the current label using the Aff-Wild2 dataset. We show through experiments that the well-trained CAPNet outperforms the baseline of the second challenge of the Affective Behavior Analysis in-the-wild (ABAW2) Competition by predicting affective valence and arousal only with past facial images one-third of a second earlier. Therefore, in real-time application, CAPNet can reliably predict affective valence and arousal only with past data.The code is publicly available. 1

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Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Cited by 2 publications

References 0 publications

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

Causal affect prediction model using a facial image sequence

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