Lie detection

This is a lie detection model that detects lies from facial, audio, and textual input

Lie detection with facial input

Per-frame action unit(AU) and eye gaze vectors are extracted with openFace(https://github.com/TadasBaltrusaitis/OpenFace)

Lie detection with audio input

Per-frame MFCCs, speech frequencies are extracted with openSmile(https://www.audeering.com/research/opensmile/)

Lie detection with textual input

Work-in-progress

Util functions

• Eye blinking counting

• FER (Facial Emotional Recognition)

• Gaze visualizer

• MFCC visualizer

Speaker Identification with machine learning

Follow the below steps to execute this feature,

• Execute SpeakerIdentification.py file.
• Install Required modules.

Things to do,

• Change all the paths in code as per your directory.
• Four options are there when you run SpeakerIdentification.py i)Record audio for training ii)Train Model iii) Record Audio for Testing iv) Test Model(Follow the same order).
• Store your audio recorded for training in training_set and testing audio in testing_set Folder.
• training_set_addition.txt use this file to append trained files and testing_set_addition.txt for appending test files.

Dataset

We used 60 truth and 61 deceptive videos from the testimonials dataset to train models Videos with both processed MFCC and Action Units(AU) are in the dataset folder.

• MFCC annotated dataset in Wavs and AU annotated dataset in Clips
• For segmented for model training, access processed

The raw dataset can be found here: https://web.eecs.umich.edu/~mihalcea/downloads.html Datasets labeled and annotated with Openface and Opensmile can be found here: https://drive.google.com/drive/folders/1_8pz1Te49nzkBE6FTizZfdNIt2hFBJn_?usp=sharing

Additional dataset

The MU3D database is also referenced in the model for testing purposes. It is available for download here: https://sc.lib.miamioh.edu/handle/2374.MIA/6067

Paper Reference

[1]Feng, Kai Jiabo. DeepLie: Detect Lies with Facial Expression (Computer Vision), Standford CS230, 2021, http://cs230.stanford.edu/projects_spring_2021/reports/0.pdf

[2]Khan, Wasiq, et al. Deception in the Eyes of Deceiver: A Computer Vision and Machine Learning Based Automated Deception Detection, Science Direct, May 2021, https://doi.org/10.1016/j.eswa.2020.114341.

[3]Shen, Xunbing, et al. Catching a Liar Through Facial Expression of Fear, Frontiers, 8 June 2021, https://www.frontiersin.org/articles/10.3389/fpsyg.2021.675097/full.

[4]Umut S¸ en, M., and Veronica P ´ erez-Rosas, et al. “Https://Sci-Hub.se/10.1109/TAFFC.2020.3015684.” Multimodal Deception Detection Using Real-Life Trial Data, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING , 2020, https://sci-hub.se/10.1109/TAFFC.2020.3015684.

[5]Venkatesh, Sushma, et al. Robust Algorithm for Multimodal Deception Detection, 2019 IEEE Conference, 2019, https://sci-hub.se/10.1109/MIPR.2019.00108.

[6]Zhang, Jiaxuan. Multimodal Deception Detection Using Automatically Extracted Acoustic, Visual, and Lexical Features, Columbia University, 2020, http://www.cs.hunter.cuny.edu/~slevitan/papers/interspeech_multimodal_deception_2020.pdf.