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

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