Decoding Identity Through Text-based Human Micro-expressions: A Novel Approach in Social Behavioral Biometrics

Wahid, Zaman

Decoding Identity Through Text-based Human Micro-expressions: A Novel Approach in Social Behavioral Biometrics

dc.contributor.advisor	Gavrilova, Marina
dc.contributor.author	Wahid, Zaman
dc.contributor.committeemember	Farhad, Maleki
dc.contributor.committeemember	John Jacobson Jr., Michael
dc.date	2023-11
dc.date.accessioned	2023-06-27T22:10:20Z
dc.date.available	2023-06-27T22:10:20Z
dc.date.issued	2023-06
dc.description.abstract	In recent years, Social Behavioral Biometrics (SBB) has gained prominence due to the dramatic changes in the way people socialize in this technologically-advanced era. The reliance on Online Social Networks (OSN) for formal and informal social interactions has become the norm. This thesis introduces a novel SBB trait, human micro-expression, for online person identification. An emotion detection model is initially developed to extract Parrott’s primary emotion scores from OSN users’ writing samples posted on Twitter. The corresponding emotion-progression features are extracted using an original technique that turns users’ microblogs into emotion signals. The Dynamic Time Warping (DTW) algorithm is utilized to facilitate the process of emotion-progression feature extraction across OSN users’ emotion signals trajectories. Then, a unimodal SBB system based on the proposed human micro-expression biometric is implemented, leveraging rank-level weighted Borda count to improve the performance of person identification. Furthermore, a multimodal SBB system is proposed that incorporates the proposed SBB trait into original SBB traits in state-of-the-art. To evaluate the effectiveness of the proposed system, a proprietary benchmark dataset consisting of 250 Twitter users is employed. The experimental results demonstrate that the novel human micro-expression trait exhibits strong distinguishability among OSN users and can be used for person identification. Moreover, the study reveals that the proposed social behavioral biometric outperforms the majority of original SBB traits, indicating its potential value in future research and applications. The proposed multimodal SBB system exhibits superior performance compared to existing state-of-the-art multimodal SBB systems, further emphasizing the utility of incorporating the human micro-expression trait in multimodal SBB systems to improve person identification performance. This thesis contributes to the burgeoning field of social behavioral biometrics, with the potential for significant advancements in future research on person identification and online security.
dc.identifier.citation	Wahid, Z. (2023). Decoding identity through text-based human micro-expressions: a novel approach in social behavioral biometrics (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/116672
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/41515
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Social Behavioral Biometrics
dc.subject	Online User Authentication
dc.subject	Biometric Security
dc.subject	Natural Language Processing
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Computer Science
dc.title	Decoding Identity Through Text-based Human Micro-expressions: A Novel Approach in Social Behavioral Biometrics
dc.type	master thesis
thesis.degree.discipline	Computer Science
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.