The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions

The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions

Examining art:dissociating pattern and perceptual influences on oculomotor behaviour

When observing art the viewer’s understanding results from the interplay between the marks made on the surface by the artist and the viewer’s perception and knowledge of it. Here we use a novel set of stimuli to dissociate the influences of the marks on the surface and the viewer’s perceptual experience upon the manner in which the viewer inspects art. Our stimuli provide the opportunity to study situations in which (1) the same visual stimulus can give rise to two different perceptual experiences in the viewer, and (2) the visual stimuli differ but give rise to the same perceptual experience in the viewer. We find that oculomotor behaviour changes when the perceptual experience changes. Oculomotor behaviour also differs when the viewer’s perceptual experience is the same but the visual stimulus is different. The methodology used and insights gained from this study offer a first step toward an experimental exploration of the relative influences of the artist’s creation and viewer’s perception when viewing art and also toward a better understanding of the principles of composition in portraiture

Visual condition: Frequency of valid answers.

<p>Frequency distribution of the number of expressions with a given number of valid answers for the visual condition. Since the maximum number of valid answers for each expression is 100 (10 models * 10 participants), expressions were grouped together resulting in group increments of 10.</p

Visual condition: Average naturalness scores for each model and their corresponding confidence intervals.

<p>Mean naturalness scores for each model and their corresponding confidence intervals for the visual condition. Grey horizontal line indicates the mean naturalness ratings over all models.</p

Context condition: Frequency of number of valid answers.

<p>Frequency distribution of the number of expressions with a given number of valid answers for the context condition. Maximum number of valid answers is 10 as there were 10 participants.</p

Visual condition: Frequency of naturalness scores for each expression stimulus.

<p>Frequency distribution of participants' naturalness ratings pooled over participants and expressions for the visual condition. Naturalness score 1 means “extremely posed facial expression” whereas 5 means “natural expression as it would occur during a conversation”.</p

Visual condition: Mean number of valid answers per each actor.

<p>Mean number of valid answers for each of the ten models sorted in descending order. Error bars present uncorrected confidence intervals.</p

Visual condition: Frequency of valid answers for all models for best expressions.

<p>Frequency distribution of the number of valid answers for each of the ten models for the expressions with the highest number of valid answers for the visual condition. The abbreviations of the expressions are the following: disrel = reluctant disagreeing, discon = considered disagreeing, ncon = not convinced, disag = disagreeing, reco = thinking considering, agcons = considered agreeing.</p