8th International Symposium on
Applied Engineering and Sciences (SAES2020)
Virtual Conference / 12th–19th December 2020

Detection of Lying Situations in Liar Corpus
Shohei Takabatake1 *, Kazutaka Shimada1 and Takeshi Saitoh2
1

Artificial Intelligence, Kyushu Institute of Technology, Iizuka Fukuoka, Japan
Design and Informatics, Kyushu Institute of Technology, Iizuka Fukuoka, Japan*Corresponding author’s e-mail: s_takabatake@pluto.ai.kyutech.ac.jp
Keywords: —Lie, Detection, Corpus, OpenPose, OpenFace

2Introduction: Recognizing lies in human interaction is one of the most important and challengingtasks in artificial intelligence. For example, it leads to the development of a human-computerinteraction system that detects a liar in a conversation. In another example, some systems in thefuture might occasionally need to tell a lie for not hurting user’s feelings, namely a white lie for troublefree conversations. In this paper, we apply machine learning techniques, support vector machineswith features extracted using OpenPose [1] and OpenFace [2], to our corpus. We discuss the resultsof the machine learning approaches in our corpus.
Approach: We describe our detection model of lying situations. We apply a machine learning
method to the task. Focusing on the facial expressions that we have confirmed in previous study [3],we extracted features from videos. We extract the features using the coordinates of faces detectedby OpenPose [1]. In addition, using OpenFace [2] which can estimate facial expressions, we extract
the Facial Action Units which is the smallest unit of facial movement such as movements of mouthcorner and eyes. The specific features are shown below.
OpenPose: Standard deviation of coordinate points, mean and standard deviation of movementof coordinate points (eyes, nose, neck, shoulders, ears)

OpenFace: Appearance rate and strength average Facial Action Units (eyebrow movement,eyelid movement, lip movement, dimple, blink)

Results and Discussion: We used SVM as the machine learning approach. We evaluated the two
detection methods with leave-one-out cross-validation. Table 1 shows the experimental result. The
method with OpenPose or OpenFace cannot outperform that with LBP-TOP features in previous
study [3]. One reason was that the accuracy of OpenPose or OpenFace was not always enough forthe feature extraction.
Applying neural network based approaches, such as a CNN to the feature extraction and a RNN tothe detection task, are prospective improved points in future work. On the other hand, for generatinga strong model, the neural network models tend to need a large volume of data. Therefore, we needto increase the size of our corpus for the purpose.
Table. 1: The accuracy, recall,and precision for the detection task.
FeaturesAccuracy

Lie
Truth
RecallPrecision
RecallPrecision
LBP-TOP [3]
0.55
0.55
0.57
0.54
0.53
OpenPose
0.50
0.500.63
0.50
0.37
OpenFace
0.50
0.50
0.46
0.50
0.54
[1] Z Cao, G Hidalgo, T Simon, SE Wei, and Y Sheikh. Openpose: Realtime multi-person 2d pose estimation
using part affinity fields. arxiv 2018. arXiv preprint arXiv:1812.08008, 1812.
[2] Tadas Baltrusaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency. Openface 2.0: Facial
behavior analysis toolkit. In 2018 13th IEEE International Conference on Automatic Face & Gesture
Recognition (FG 2018), pages 59–66. IEEE, 2018.
[3] S. Takabatake, K. Shimada, and T. Saitoh. Construction of a liar corpus and detection of lying situations.
In 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19thInternational Symposium on Advanced Intelligent Systems (ISIS), pages 971–976, 2018.