Poker Bluff Detection Dataset Based on Facial Analysis
Jacob Feinland, Jacob Barkovitch, Dokyu Lee, and 2 more authors
In Image Analysis and Processing – ICIAP 2022 Jan 2022
Unstaged data with people acting naturally in real-world scenarios is essential for high-stakes deception detection (HSDD) research. Unfortunately, multiple HSDD studies involve staged scenarios in controlled settings with subjects who were instructed to lie. Using in-the-wild footage of subjects and analyzing facial expressions instead of invasive tracking of biological processes enables the collection of real-world data. Poker is a high-stakes game involving a deceptive strategy called bluffing and is an ideal research subject for improving HSDD techniques. Videos of professional poker tournaments online provide a convenient data source. Because proficiency in HSDD generalizes well for different high-stakes situations, findings from poker bluff detection research have the potential to transfer well to other more practical HSDD applications like interrogations and customs inspections. In the hopes of encouraging additional research on real-world HSDD, we present a novel in-the-wild dataset for poker bluff detection. To verify the quality of our dataset, we test its regression accuracy and achieve a Mean Square Error of 0.0288 with an InceptionV3 model.
