Facial information is a powerful channel for human-to-human communication. Characteristically, faces can be defined as biological objects that are four-dimensional (4D) patterns, whereby they have concurrently a spatial structure as well as temporal dynamics. The spatial characteristics of facial objects possess three dimensions (3D), namely breadth, height and importantly, depth. The temporal properties of facial objects are defined by how a 3D facial structure evolves dynamically over time; where time is referred to as the fourth dimension (4D). Our entire perspective (...) of another’s face, whether it be social, affective or cognitive perceptions, is therefore built on a combination of 3D and 4D visual cues. Counterintuitively, over the past few decades of experimental research in psychology, facial stimuli have largely been presented to participants as a spatial pattern that has been flattened into two dimensions (2D), while remaining largely static. The following review aims to advance and update facial researchers, on the recent revolution in computer-generated, realistic 4D facial models produced from real-life human subjects. We delve in-depth to summarize recent studies which have utilized facial stimuli that possess 3D structural cues (geometry and depth) and 4D temporal cues (3D structure + dynamic viewpoint and movement). In sum, we have found that higher-order perceptions such as identity, gender, ethnicity, emotion and personality, are critically influenced by the 4D characteristics embedded within natural human faces. In future, it is recommended that facial stimuli incorporate the 4D space-time perspective with the proposed time-resolved methods. (shrink)

We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...) order to help emphasise the relevant issues. To answer the question, we review a large number of empirical studies, in which samples of behaviour are used to automatically infer a range of psychological constructs, including affect and emotions, aptitudes and skills, attitudes and orientations (e.g. values and sexual orientation), personality, and disorders and conditions (e.g. depression and addiction). We also present a general perspective that can bring these disparate studies together and allow us to think clearly about their philosophical and ethical implications, such as issues related to consent, privacy, and the use of persuasive technologies for controlling human behaviour. (shrink)

Investigators increasingly need high quality face photographs that they can use in service of their scholarly pursuits—whether serving as experimental stimuli or to benchmark face recognition algorithms. Up to now, an index of known face databases, their features, and how to access them has not been available. This absence has had at least two negative repercussions: First, without alternatives, some researchers may have used face databases that are widely known but not optimal for their research. Second, a reliance on databases (...) comprised only of young white faces will lead to science that isn't representative of all the people whose tax contributions, in many cases, make that research possible. The “Face Image Meta-Database” (fIMDb) provides researchers with the tools to find the face images best suited to their research, with filters to locate databases with people of a varied racial and ethnic backgrounds and ages. Problems of representation in face databases are not restricted to race and ethnicity or age – there is a dearth of databases with faces that have visible differences (e.g., scars, port wine stains, and cleft lip and palate). A well-characterized database is needed to support programmatic research into perceivers' attitudes, behaviors, and neural responses to anomalous faces. The “ChatLab Facial Anomaly Database” (CFAD) was constructed to fill this gap, with photographs of faces with visible differences of various types, etiologies, sizes, locations, and that depict individuals from various ethnic backgrounds and age groups. Both the fIMDb and CFAD are available from cliffordworkman[dot]com/resources/. (shrink)