Scientific publications
Read about the research that supports the FaceReader Ecosystem
Over the past 20+ years, our facial coding platform and its embedded technologies have been the subject as well as the preferred instrument for numerous accredited scientific studies. Below we present a comprehensive overview of the literature that has emerged from these studies, highlighting and validating the cutting-edge technology of FaceReader Online.
2015
37 citations
Implicit Attitude Toward Caregiving : The Moderating Role of Adult Attachment Styles
De Carli, Tagini, Sarracino, Santona, Parolin
Attachment and caregiving are separate motivational systems that share the common evolutionary purpose of favoring child security. In the goal of studying the processes underlying the transmission of attachment styles, this study focused on the role of adult attachment styles in shaping preferences toward particular styles of caregiving. We hypothesized a correspondence between attachment and caregiving styles: we expect an individual to show a preference for a caregiving behavior coherent with his/her own attachment style, in order to increase the chance of passing it on to offspring. We activated different representations of specific caregiving modalities in females, by using three videos in which mothers with different Adult Attachment states of mind played with their infants. Participants’ facial expressions while watching were recorded and analyzed with FaceReader software. After each video, participants’ attitudes toward the category “mother” were measured, both explicitly and implicitly . Participants’ adult attachment styles predicted attitudes scores, but only when measured implicitly. Participants scored higher on the ST-IAT after watching a video coherent with their attachment style. No effect was found on the facial expressions of disgust. These findings suggest a role of adult attachment styles in shaping implicit attitudes related to the caregiving system.
2015
58 citations
EEVEE: the Empathy-Enhancing Virtual Evolving Environment.
Jackson, Michon, Geslin, Carignan, Beaudoin
Empathy is a complex emotional and cognitive faculty often impaired in various psychopathologies, such as schizophrenia, and challenging to measure in real-world contexts. To address this, we developed the Empathy-Enhancing Virtual Evolving Environment , a platform comprising: avatars capable of expressing emotions at varying intensities based on the Facial Action Coding System ; systems for measuring observers’ physiological responses ; and a multimodal interface linking avatar behavior to observer responses. Validation data indicate that healthy adults can discern different negative emotions, including pain, expressed by avatars at varying intensities. Additionally, masking parts of an avatar’s face does not hinder the detection of different pain levels. EEVEE offers a unique tool to study and potentially modulate empathy in an ecological manner across various populations, notably those with neurological or psychiatric disorders.
2015
53 citations
Charitable Giving, Emotions, and the Default Effect
Noussair & Habetinova
We report an experiment to study the effect of defaults on charitable giving. In three different treatments, participants face varying default levels of donation. In three other treatments that are paired with the first three, they receive the same defaults, but are informed that defaults are thought to have an effect on their donation decisions. The emotional state of all individuals is monitored throughout the sessions using Facereading software, and some participants are required to report their emotional state after the donation decision. We find that the level at which a default is set has no effect on donations, and informing individuals of the possible impact of defaults also has no effect. Individuals who are happier and in a more positive overall emotional state donate more. Donors experience a negative change in the valence of their emotional state subsequent to donating, when valence is measured with Facereading software. This contrasts with the self-report data, in which donating correlates with a more positive reported subsequent emotional state.
2015
8 citations
Who do you want to be? Real-time face swap
T.M. den Uyl, H.E. Tasli, P. Ivan, M. Snijdewind
This demonstration paper presents a face swap application where two people’s faces are automatically exchanged in real-time without any calibration or training. This is performed using the Active Appearance Models technique. A realistic visualization is achieved using an adaptive texture sampling technique. The face swap is performed irrespective of the sex, age or ethnicity of the subject in front of the camera. This application is intended for gaming, shopping, educational or entertainment purposes and will be presented in a real-time setup during the demo session.
2014
62 citations
Moral violations reduce oral consumption
Chan, van Boven, Andrade, Ariely
Consumers frequently encounter moral violations in everyday life, such as through media portrayals of crime and deception, news reports of corporate fraud, and gossip about unethical behavior. This study investigates how exposure to moral violations influences consumption behavior. Given that moral violations elicit disgust—a signal of contamination that typically reduces oral consumption—the researchers hypothesized that exposure to moral violations would lead to decreased consumption. Across three experiments, participants consumed less water and chocolate milk when: watching a film depicting incest, writing about cheating or theft, and listening to a report on fraud and manipulation. These findings suggest that “moral disgust” affects consumption similarly to core disgust, highlighting the connection between moral violations, emotions, and consumer behavior.
2014
3 citations
Turkish presidential elections trt publicity speech facial expression analysis
H.E. Tasli, P. Ivan
In this paper, the facial expressions of the three Turkish presidential candidates—Demirtas, Erdogan, and Ihsanoglu—are analyzed during their publicity speeches broadcasted on TRT on August 3, 2014. The analysis utilizes FaceReader, which employs active appearance models to achieve 3D facial modeling. Over 500 landmark points are tracked and analyzed to determine the candidates’ facial expressions throughout their speeches. All source videos and the data are publicly available for research purposes.