Abstract
The contribution addresses problem of detecting a deception when interrogation is going on by taking the thermal images of the face. When the person is lying, due to stress on his face, the blood flow in periorbital tissue and cutaneous muscles increases, which ultimately results in the higher blood flow in the respective areas. In the proposed work, we have collected the dataset of such thermal images and developed algorithms to extract the features from the dataset generated in order to be able to train the neural network model which will in turn classify the input image or frame of video as a deception or truth. Using the proposed approach, obtained F1 score for baseline , truth, direct lie and indirect lie is 54%, 46%, 67%, respectively, and overall accuracy is 60.53%.
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Acknowledgements
We appreciate S. H. Bhandari, Minal Parchand, Pankhudi Bhonsle and Komal Kotyal for building concrete foundation which helped us to accomplish this work and also to Department of Computer Science and Engineering WCE, Sangli for continuous support and valuable guidance.
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Kodavade, P., Bhandigare, S., Kadam, A., Redekar, N., Kamble, K.P. (2021). Lie Detection Using Thermal Imaging Feature Extraction from Periorbital Tissue and Cutaneous Muscle. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-33-4543-0_68
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DOI: https://doi.org/10.1007/978-981-33-4543-0_68
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