Prediction of the Leadership Style of an Emergent Leader Using Audio and Visual Nonverbal Features

Beyan, Çiğdem; Capozzi, Francesca; Becchio, Cristina; Murino, Vittorio

doi:10.1109/tmm.2017.2740062

Cited by 48 publications

(44 citation statements)

References 63 publications

(361 reference statements)

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“…For example, it has been theorized that the dynamics of looking behaviors during real‐life interactions represent a tacit marker of group social processes, as both nonhuman primates and human observers viewing video recordings of group interactions look more at high‐status individuals (i.e., leaders) relative to low‐status individuals (i.e., nonleaders). Capozzi and colleagues tested this intuition during real‐life group interactions and found that group leaders could be reliably identified based on the dynamics of group‐looking behaviors, as they were looked at more and engaged in more mutual gaze episodes relative to other group members . Extending this work, Capozzi and Ristic recently found that such implicit social dynamics reliably influenced subsequent social behaviors, with the amount of time one was being looked at during a group interaction predicting the magnitude of gaze following elicited by their gaze cues in subsequent encounters.…”

Section: The Three Core Processesmentioning

confidence: 97%

“…In contrast, small groups, typically defined as those composed of 3–5 individuals engaged in a common activity, elicit and afford social interactions that require efficient handling of individual members’ social cues . Here, the three routes of processing appear to allow for the dynamic allocation of social attention to each group member depending on their identity, status, and/or situational context . In this paper, we identified the likely mechanisms that may facilitate the behavioral and processing flexibilities that are required by multiagent contexts, with further research needed to build a more consolidated understanding of how attentional operations facilitate social interactions in large and small groups.…”

Section: Attention As a Gate For Social Interactionsmentioning

confidence: 99%

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How attention gates social interactions

Capozzi

Ristic

2018

Annals of the New York Academy of Sciences

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Social interactions are at the core of social life. However, humans selectively choose their exchange partners and do not engage in all available opportunities for social encounters. In this review, we argue that attentional systems play an important role in guiding the selection of social interactions. Supported by both classic and emerging literature, we identify and characterize the three core processes-perception, interpretation, and evaluation-that interact with attentional systems to modulate selective responses to social environments. Perceptual processes facilitate attentional prioritization of social cues. Interpretative processes link attention with understanding of cues' social meanings and agents' mental states. Evaluative processes determine the perceived value of the source of social information. The interplay between attention and these three routes of processing places attention in a powerful role to manage the selection of the vast amount of social information that individuals encounter on a daily basis and, in turn, gate the selection of social interactions.

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Section: The Three Core Processesmentioning

confidence: 97%

Section: Attention As a Gate For Social Interactionsmentioning

confidence: 99%

How attention gates social interactions

Capozzi

Ristic

2018

Annals of the New York Academy of Sciences

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“…For example, Sanchez-Cortes et al [51] proposed a method to detect emergent leaders in a group discussion using handcrafted audio and visual features. Beyan et al [5] applied multiple kernel learning to similar features to predict the leadership styles of emergent leaders. Hoque et al [32] leveraged multimodal behavioral data to generate instructive feedback in the context of training for job interviews.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Nihei et al [45] leveraged head pose information and speech features and concluded that the combination of these features achieved the best accuracy for detecting important utterances compared to unimodal methods. Beyan et al [5] combined the pose and speech features with the gaze information and reported the effectiveness of the multimodal input. Hoque et al [32] exploited facial expressions for the training of interviewees.…”

Section: Multimodal Inputmentioning

confidence: 99%

REsCUE

Arakawa

Yakura

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Executive coaching has been drawing more and more attention for developing corporate managers. While conversing with managers, coach practitioners are also required to understand internal states of coachees through objective observations. In this paper, we present REsCUE, an automated system to aid coach practitioners in detecting unconscious behaviors of their clients. Using an unsupervised anomaly detection algorithm applied to multimodal behavior data such as the subject's posture and gaze, REsCUE notifies behavioral cues for coaches via intuitive and interpretive feedback in real-time. Our evaluation with actual coaching scenes confirms that REsCUE provides the informative cues to understand internal states of coachees. Since REsCUE is based on the unsupervised method and does not assume any prior knowledge, further applications beside executive coaching are conceivable using our framework. CCS CONCEPTS• Human-centered computing → Computer supported cooperative work; HCI design and evaluation methods; • Information systems → Multimedia and multimodal retrieval; * These authors contributed equally and are ordered alphabetically † Also with University of Tsukuba, Japan. Figure 1: REsCUE detects the behavioral cues of the coachee and notifies the coach in real-time to help the coach understand the internal states of the coachee.

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“…Further studies improved emergent leadership detection on the PAVIS dataset by using deep visual activity features [9], or by employing sequential analysis [8]. Apart from emergent leadership detection, the dataset has also been used to predict the leadership style of emergent leaders [5,9].…”

Section: Introductionmentioning

confidence: 99%

Emergent Leadership Detection Across Datasets

Müller

Bulling

2019

2019 International Conference on Multimodal Interaction

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Automatic detection of emergent leaders in small groups from nonverbal behaviour is a growing research topic in social signal processing but existing methods were evaluated on single datasets -an unrealistic assumption for real-world applications in which systems are required to also work in settings unseen at training time. It therefore remains unclear whether current methods for emergent leadership detection generalise to similar but new settings and to which extent. To overcome this limitation, we are the first to study a cross-dataset evaluation setting for the emergent leadership detection task. We provide evaluations for within-and cross-dataset prediction using two current datasets (PAVIS and MPI-IGroupInteraction), as well as an investigation on the robustness of commonly used feature channels (visual focus of attention, body pose, facial action units, speaking activity) and online prediction in the cross-dataset setting. Our evaluations show that using pose and eye contact based features, cross-dataset prediction is possible with an accuracy of 0.68, as such providing another important piece of the puzzle towards emergent leadership detection in the real world. KEYWORDS social signal processing, emergent leadership detection, nonverbal behaviour

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Prediction of the Leadership Style of an Emergent Leader Using Audio and Visual Nonverbal Features

Cited by 48 publications

References 63 publications

How attention gates social interactions

How attention gates social interactions

REsCUE

Emergent Leadership Detection Across Datasets

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