Name
Abstract | ||
|---|---|---|
In this paper, we explore the hypothesis that multimodal features as well as demographic information can play an important role in increasing the performance of automatic lie detection. We introduce a large, multimodal deception detection dataset balanced across genders, and we analyze the patterns associated with the thermal, linguistic, and visual responses of liars and truth-tellers. We show that our multimodal noncontact deception detection approach can lead to a performance in the range of 60%–80%, with different modalities, different genders, and different domain settings playing a role in the accuracy of the system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/mmul.2018.2883128 | IEEE MultiMedia |
Keywords | Field | DocType |
Feature extraction,Linguistics,Visualization,Cameras,Thermal sensors,Interviews,Thermal analysis | Modalities,Deception,Visualization,Computer science,Lie detection,Feature extraction,Human–computer interaction,Thermal sensors | Journal |
Volume | Issue | ISSN |
26 | 3 | 1070-986X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Abouelenien | 1 | 56 | 7.37 |
| Mihai G. Burzo | 2 | 38 | 4.54 |
| Verónica Pérez-Rosas | 3 | 154 | 11.74 |
| Rada Mihalcea | 4 | 6460 | 445.54 |
| Sun, Haitian | 5 | 13 | 4.28 |
| Bohan Zhao | 6 | 0 | 0.34 |