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

Objective Classes for Micro-Facial Expression Recognition

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for journal revie

You Can\u27t Handle the Truth! Trial Juries and Credibility

Every now and again, we get a look, usually no more than a glimpse, at how the justice system really works. What we see—before the sanitizing curtain is drawn abruptly down—is a process full of human fallibility and error, sometimes noble, more often unfair, rarely evil but frequently unequal. The central question, vital to our adjudicative model, is: How well can we expect a jury to determine credibility through the ordinary adversary processes of live testimony and vigorous impeachment? The answer, from all I have been able to see is: not very well

You Can\u27t Handle the Truth! Trial Juries and Credibility

Every now and again, we get a look, usually no more than a glimpse, at how the justice system really works. What we see—before the sanitizing curtain is drawn abruptly down—is a process full of human fallibility and error, sometimes noble, more often unfair, rarely evil but frequently unequal. The central question, vital to our adjudicative model, is: How well can we expect a jury to determine credibility through the ordinary adversary processes of live testimony and vigorous impeachment? The answer, from all I have been able to see is: not very well

Subjective cues to deception/honesty in a high stakes situation: An exploratory approach

This is an Accepted Manuscript of an article published by Taylor & Francis in The Journal of Psychology: Interdisciplinary and Applied on 7/5/2014 available online: http://wwww.tandfonline.com/10.1080/00223980.2014.911140The low ecological validity of much of the research on deception detection is a limitation recognised by researchers in the field. Consequently, the present studies investigated subjective cues to deception using the real life, high stakes situation of people making public appeals for help with missing or murdered relatives. It was expected that cues related to affect would be particularly salient in this context. Study 1 was a qualitative investigation identifying cues to deception reportedly used by people accurate at detecting deception. Studies 2 and 3 were then empirical investigations which mainly employed the cues reported in Study 1. A number of subjective cues were found to discriminate between honest and deceptive appeals, including some previously unidentified cues, and cues likely to be context-specific. Most could be categorised under the themes of authenticity of emotion, and negative and positive affective reactions to the appealer. It is concluded that some cues to deception may emerge only in real life, high stakes situations; however, it is argued that some of these may be influenced by observers’ perceptions of the characteristics of offenders, rather than acts of deception per se.ESRC grant number ES/I90316X/