4D Cardiff Conversation Database (4D CCDb): A 4D database of natural,dyadic conversations

The 4D Cardiff Conversation Database (4D CCDb) is the first 4D (3D Video) audio-visual database containing natural conversations between pairs of people. This publicly available database contains 17 conversations which have been fully annotated for speaker and listener activity: conversational facial expressions, head motion, and verbal/non-verbal utterances. It can be accessed at http://www.cs.cf.ac.uk/CCDb. In this paper we describe the data collection and annotation process. We also provide results of a baseline classification experiment distinguishing frontchannel from backchannel smiles, using 3D Active Appearance Models for feature extraction, polynomial fitting for representing the data as 4D sequences, and Support Vector Machines for classification. We believe this expression-rich, audio-visual database of natural conversations will make a useful contribution to the computer vision, affective computing, and cognitive science communities by providing raw data, features, annotations, and baseline comparisons

Cross-Corpora Study of Smiles and Laughter Mimicry in Dyadic Interactions

In this paper, we present preliminary results of our ongoing work on cross-corpora analyses of smiles and laughter mimicry. For this, instead of recording new data, we leverage the ones produced and available. We analyze smiles and laughs mimicry in three different datasets and show results similar to our previous work.The data used here can be accessed at: https://doi.org/10.5281/zenodo.3820510

Cross-Corpora Study of Smiles and Laughter Mimicry in Dyadic Interactions

In this paper, we present preliminary results of our ongoing work on cross-corpora analyses of smiles and laughter mimicry. For this, instead of recording new data, we leverage the ones produced and available. We analyze smiles and laughs mimicry in three different datasets and show results similar to our previous work.The data used here can be accessed at: https://doi.org/10.5281/zenodo.3820510

Macro-and Micro-Expressions Facial Datasets: A Survey

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro-and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application

SEWA DB: A rich database for audio-visual emotion and sentiment research in the wild

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are becoming indispensable part of our life more and more. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

Twente Debate Corpus - A Multimodal Corpus for Head Movement Analysis

This paper introduces a multimodal discussion corpus for the study into head movement and turn-taking patterns in debates. Given that participants either acted alone or in a pair, cooperation and competition and their nonverbal correlates can be analyzed. In addition to the video and audio of the recordings, the corpus contains automatically estimated head movements, and manual annotations of who is speaking and who is looking where. The corpus consists of over 2 hours of debates, in 6 groups with 18 participants in total. We describe the recording setup and present initial analyses of the recorded data. We found that the person who acted as single debater speaks more and also receives more attention compared to the other debaters, also when corrected for the time speaking.We also found that a single debater was more likely to speak after a team debater. Future work will be aimed at further analysis of the relation between speaking and looking patterns, the outcome of the debate and perceived dominance of the debaters