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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Deception Detection in Videos

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Zhe Wu

University of Maryland College Park


Bharat Singh

University of Maryland College Park


Larry Davis

University of Maryland College Park


V. Subrahmanian

Dartmouth College


DOI:

10.1609/aaai.v32i1.11502


Abstract:

We present a system for covert automated deception detection using information available in a video. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible.

Topics: AAAI

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HOW TO CITE:

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian Deception Detection in Videos Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian Deception Detection in Videos AAAI 2018, .

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian (2018). Deception Detection in Videos. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. Deception Detection in Videos. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. 2018. Deception Detection in Videos. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. (2018) "Deception Detection in Videos", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian, "Deception Detection in Videos", AAAI, p., 2018.

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. "Deception Detection in Videos". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. "Deception Detection in Videos". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Zhe Wu||Bharat Singh||Larry Davis||V. Subrahmanian. Deception Detection in Videos. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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