Real-time Facial Expression Recognition to Track Non-verbal Behaviors as Lie Indicators During Interview

Arif Budi Setiawan, Kaspul Anwar, Laelatul Azizah, Adhi Prahara

Abstract


During interview, a psychologist should pay attention to every gesture and response, both verbal and nonverbal language/behaviors, made by the client. Psychologist certainly has limitation in recognizing every gesture and response that indicates a lie, especially in interpreting nonverbal behaviors that usually occurs in a short time. In this research, a real time facial expression recognition is proposed to track nonverbal behaviors to help psychologist keep informed about the change of facial expression that indicate a lie. The method tracks eye gaze, wrinkles on the forehead, and false smile using combination of face detection and facial landmark recognition to find the facial features and image processing method to track the nonverbal behaviors in facial features. Every nonverbal behavior is recorded and logged according to the video timeline to assist the psychologist analyze the behavior of the client. The result of tracking nonverbal behaviors of face is accurate and expected to be useful assistant for the psychologists.

Keywords


Lie detector; Facial expression; Nonverbal behaviors; Facial landmark; Face detection

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References


S. S. Willis, “Konseling individual teori dan praktek,” Bandung Alf., vol. 79, 2004.

J. P. Rosenfeld, “Alternative views of Bashore and Rapp’s (1993) alternatives to traditional polygraphy: A critique.,” 1995.

A. Mottelson, J. Knibbe, and K. Hornbæk, “Veritaps: Truth Estimation from Mobile Interaction,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018, p. 561.

S. Neeta and S. Bhatia, “Facial Expression Recognition,” Int. J. Comput. Sci. Eng., vol. 2, 2010.

A. S. Dhavalikar and R. K. Kulkarni, “Face detection and facial expression recognition system,” in 2014 International Conference on Electronics and Communication Systems (ICECS), 2014, pp. 1–7.

A. T. Lopes, E. de Aguiar, A. F. De Souza, and T. Oliveira-Santos, “Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order,” Pattern Recognit., vol. 61, pp. 610–628, Jan. 2017.

J. Navarro and M. Karlins, What every body is saying. HarperCollins Publishers, 2008.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, p. I-511-I-518.

M. Uričár, V. Franc, and V. Hlaváč, “Detector of Facial Landmarks Learned by the Structured Output SVM,” in VISAPP ’12: Proceedings of the 7th International Conference on Computer Vision Theory and Applications, 2012, vol. 1, pp. 547–556.

J. Matas, C. Galambos, and J. Kittler, “Robust Detection of Lines Using the Progressive Probabilistic Hough Transform,” Comput. Vis. Image Underst., vol. 78, no. 1, pp. 119–137, Apr. 2000.




DOI: https://doi.org/10.31763/simple.v1i1.85

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Copyright (c) 2023 Arif Budi Setiawan, Kaspul Anwar, Laelatul Azizah, Adhi Prahara

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Signal and Image Processing Letters

ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website : https://simple.ascee.org/index.php/simple/
Email 1 : simple@ascee.org
Email 2 : azhari@ascee.org


 

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