Poker Bluff Detection Dataset Based on Facial Analysis

Poker is a high-stakes game involving a deceptive strategy called bluffing and is an ideal research subject for improving high-stakes deception detection (HSDD) techniques like those used by interrogators. Multiple HSDD studies involve staged scenarios in controlled settings with subjects who were told to lie. Scenarios like staged interrogations are inherently poor data sources for HSDD because the subjects will naturally respond differently than someone who actually risks imprisonment, or in the case of poker, loses great sums of money. Thus, unstaged data is a necessity. Unlike traditional HSDD methods involving invasive measurement of biometric data, using video footage of subjects allows for analyzing people’s natural deceptions in real high-stakes scenarios using facial expressions. Deception detection generalizes well for different high-stakes situations, so the accessibility of data in videos of poker tournaments online is convenient for research on this subject. In the hopes of encouraging additional research on real-world HSDD, we present a novel in-the-wild dataset using four different videos from separate professional poker tournaments, totaling 48 minutes. These videos contain great variety in head poses, lighting conditions, and occlusions. We used players’ cards and bets to manually label bluffs and then extracted facial expressions in over 31,000 video frames containing face images from 25 players. We used the dataset to train a state-of-the-art convolutional neural network (CNN) to identify bluffing based on face images, achieving high accuracy for a baseline model. We believe this dataset will allow future in-the-wild bluff detection research to achieve higher deception detection rates, which will enable the development of techniques for more practical applications of HSDD such as in police interrogations and customs inspections.https://orb.binghamton.edu/research_days_posters_2021/1028/thumbnail.jp

The obstetrics team: Midwives teaching residents and medical students on the labor and delivery unit

In response to the new standards for resident work hours issued in 2003, Baystate Medical Center in Springfield, MA developed a program for midwifery involvement in resident and medical education. The Obstetrics Team consists of a midwife teaching first-year residents in obstetrics/gynecology and emergency medicine and third-year medical students on the labor and delivery unit. This program has successfully addressed the need for resident and medical education as well as service provision created by reduced resident work hours and provides a useful model for other institutions

Making it work: Successful collaborative practice

There are three major examples of collaborative programs between certified nurse-midwives (CNMs) and obstetrician-gynecologists at Baystate Medical Center in Springfield, Massachusetts, within the Department of Obstetrics and Gynecology. One program is a midwifery practice that serves a diverse population in a hospital-based office, four neighborhood health centers, and a correctional facility. Another program provides a triage function for patients who present to the hospital with obstetric or gynecologic problems. The third program introduces a team approach to the education of residents with a CNM having primary responsibility for teaching normal obstetrics to first-year residents and medical students in collaboration with attending physicians. Keys to success include an understanding of the principles of collaborative practice, the use of a detailed practice agreement between midwives and attending physicians, keeping open lines of communication, understanding and accepting differing philosophies of practice, and, most importantly, maintaining trust across all levels of providers