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Binghamton University Research

A High-Resolution Spontaneous 3D Dynamic Facial Expression Database

Abstract—Facial expression is central to human experience. Its efficient and valid measurement is a challenge that automated facial image analysis seeks to address. Most publically available databases are limited to 2D static images or video of posed facial
behavior. Because posed and un-posed (aka “spontaneous”) facial expressions differ along several dimensions including complexity and timing, well-annotated video of un-posed facial behavior is needed. Moreover, because the face is a three-dimensional deformable object, 2D video may be insufficient, and therefore 3D video archives are needed. We present a newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and
motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.

Excerpt from Bing News, May 5, 2020, by Kyle Polidore:

"For both previous databases, participants were asked to perform emotions such as fear, sadness or happiness, meaning that all of the emotions were performative rather than genuine. To make the data more useful to fellow researchers, Yin partnered with Binghamton University psychology professor Peter Gerhardstein and artist-in-residence Andy Horowitz (an actor and founder of the acrobatic dance team Galumpha) to help elicit spontaneous facial expressions for his third dataset, BP4D."

A Systematic Review of eHealth Interventions to Promote Physical Activity in Obese or Overweight Adults


Use of information and communication technology to improve health, known as eHealth, is an emerging concept in healthcare that may present opportunities to promote physical activity in adults with obesity. The purpose of this research was to systematically review eHealth intervention studies to promote physical activity in adults with obesity. Five electronic databases were used. Two authors screened articles, assessed risk of bias, and extracted data independently. A qualitative data synthesis for summarizing the findings was performed using harvest plots. In the search, 2276 articles were identified, and 18 studies met all inclusion criteria. Study quality ranged from poor to good. The included studies varied in intervention technology (e.g., web-based), physical activity assessment (e.g., device-based), and control group (e.g., wait-list). Behavioral change techniques used in the included studies were consistent with some techniques (e.g., self-monitoring) known as effective in face-to-face interventions, but more efficiently employed in eHealth using information and communication technology. Overall, this systematic review showed that a web-based or physical activity monitor-based eHealth intervention had the potential to effectively promote physical activity in adults with obesity. Some recommendations for future eHealth interventions to promote physical activity in adults with obesity were provided (e.g., use of theory, accelerometers).

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