Automatic dominance detection in dyadic conversations (original) (raw)

Dominance detection in face-to-face conversations

2009

Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences.

Modeling dominance in group conversations using nonverbal activity cues

Audio, Speech, and …, 2009

Dominance -a behavioral expression of power -is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audio and visual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multicamera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audio-visual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date.

Dominance Detection in Meetings Using Easily Obtainable Features

Lecture Notes in Computer Science, 2006

We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.

Investigating automatic dominance estimation in groups from visual attention and speaking activity

2008

We study the automation of the visual dominance ratio (VDR); a classic measure of displayed dominance in social psychology literature, which combines both gaze and speaking activity cues. The VDR is modified to estimate dominance in multi-party group discussions where natural verbal exchanges occur and other visual targets such as a table and slide screen are present. Our findings suggest that fully automated versions of these measures can estimate effectively the most dominant person in a meeting and can approximate the dominance estimation performance when manual labels of visual attention are used.

A Multimodal Corpus for Studying Dominance in Small Group Conversations

We present a new multimodal corpus with dominance annotations on small group conversations. We used five-minute non-overlapping slices from a subset of meetings selected from the popular Augmented Multi-party Interaction (AMI) corpus. The total length of the annotated corpus corresponds to 10 hours of meeting data. Each meeting is observed and assessed by three annotators according to their level of perceived dominance. We analyzed the annotations with respect to dominance, status, gender and behavior. The results of the analysis reflect the findings in the social psychology literature on dominance. The described dataset provides an appropriate testbed for automatic dominance analysis.

Automatic Detection of Dominance and Expected Interest

Eurasip Journal on Advances in Signal Processing, 2010

Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.

Predicting dominance in multi-person videos

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

We consider the problems of predicting (i) the most dominant person in a group of people, and (ii) the more dominant of a pair of people, from videos depicting group interactions. We introduce a novel family of variables called Dominance Rank. We combine features not previously used for dominance prediction (e.g., facial action units, emotions), with a novel ensemble-based approach to solve these two problems. We test our models against four competing algorithms in the literature on two datasets and show that our results improve past performance. We show 2.4% to 16.7% improvement in AUC compared to baselines on one dataset, and a gain of 0.6% to 8.8% in accuracy on the other. Ablation testing shows that Dominance Rank features play a key role.

Using audio and video features to classify the most dominant person in a group meeting

2007

The automated extraction of semantically meaningful information from multi-modal data is becoming increasingly necessary due to the escalation of captured data for archival. A novel area of multi-modal data labelling, which has received relatively little attention, is the automatic estimation of the most dominant person in a group meeting. In this paper, we provide a framework for detecting dominance in group meetings using different audio and video cues. We show that by using a simple model for dominance estimation we can obtain promising results.

Using Audio and Video Features to Classify the Most Dominant Person in a

The automated extraction of semantically meaningful information from multi-modal data is becoming increasingly necessary due to the escalation of captured data for archival. A novel area of multi-modal data labelling, which has received relatively little attention, is the automatic estimation of the most dominant person in a group meeting. In this paper, we provide a framework for detecting dominance in group meetings using different audio and video cues. We show that by using a simple model for dominance estimation we can obtain promising results.

Predicting the dominant clique in meetings through fusion of nonverbal cues

2008

This paper addresses the problem of automatically predicting the dominant clique (i.e., the set of K-dominant people) in face-to-face small group meetings recorded by multiple audio and video sensors. For this goal, we present a framework that integrates automatically extracted nonverbal cues and dominance prediction models. Easily computable audio and visual activity cues are automatically extracted from cameras and microphones. Such nonverbal cues, correlated to human display and perception of dominance, are well documented in the social psychology literature. The effectiveness of the cues were systematically investigated as single cues as well as in unimodal and multimodal combinations using unsupervised and supervised learning approaches for dominant clique estimation. Our framework was evaluated on a five-hour public corpus of teamwork meetings with third-party manual annotation of perceived dominance. Our best approaches can exactly predict the dominant clique with 80.8% accuracy in four-person meetings in which multiple human annotators agree on their judgments of perceived dominance.