Detecting decision ambiguity from facial images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00324063" target="_blank" >RIV/68407700:21230/18:00324063 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/18:00324063

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/ftp/articles/cech/Jahoda-FG-2018.pdf" target="_blank" >http://cmp.felk.cvut.cz/ftp/articles/cech/Jahoda-FG-2018.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FG.2018.00080" target="_blank" >10.1109/FG.2018.00080</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detecting decision ambiguity from facial images

Popis výsledku v původním jazyce

In situations when potentially costly decisions are being made, faces of people tend to reflect a level of certainty about the appropriateness of the chosen decision. This fact is known from the psychological literature. In the paper, we propose a method that uses facial images for automatic detection of the decision ambiguity state of a subject. To train and test the method, we collected a large-scale dataset from "Who Wants to Be a Millionaire?" -- a popular TV game show. The videos provide examples of various mental states of contestants, including uncertainty, doubts and hesitation. The annotation of the videos is done automatically from on-screen graphics. The problem of detecting decision ambiguity is formulated as binary classification. Video-clips where a contestant asks for help (audience, friend, 50:50) are considered as positive samples; if he (she) replies directly as negative ones. We propose a baseline method combining a deep convolutional neural network with an SVM. The method has an error rate of 24%. The error of human volunteers on the same dataset is 45%, close to chance.

Název v anglickém jazyce

Detecting decision ambiguity from facial images

Popis výsledku anglicky

In situations when potentially costly decisions are being made, faces of people tend to reflect a level of certainty about the appropriateness of the chosen decision. This fact is known from the psychological literature. In the paper, we propose a method that uses facial images for automatic detection of the decision ambiguity state of a subject. To train and test the method, we collected a large-scale dataset from "Who Wants to Be a Millionaire?" -- a popular TV game show. The videos provide examples of various mental states of contestants, including uncertainty, doubts and hesitation. The annotation of the videos is done automatically from on-screen graphics. The problem of detecting decision ambiguity is formulated as binary classification. Video-clips where a contestant asks for help (audience, friend, 50:50) are considered as positive samples; if he (she) replies directly as negative ones. We propose a baseline method combining a deep convolutional neural network with an SVM. The method has an error rate of 24%. The error of human volunteers on the same dataset is 45%, close to chance.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

FG 2018: Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition

ISBN

978-1-5386-2335-0

ISSN

—

e-ISSN

2326-5396

Počet stran výsledku

5

Strana od-do

499-503

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Xi’an

Datum konání akce

15. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000454996700070