AestheticID: Human Identification Using Audio-Visual Preferences

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dc.contributor.advisorGavrilova, Marina
dc.contributor.authorIffath, Fariha
dc.contributor.committeememberSousa, Mario Costa
dc.contributor.committeememberTepperman, Charles
dc.date2025-02
dc.date.accessioned2024-11-18T17:33:41Z
dc.date.available2024-11-18T17:33:41Z
dc.date.issued2024-11-14
dc.description.abstractOver the last decade, Online Social Media platforms have witnessed a substantial expansion due to the extensive reliance of individuals on these communication channels. These platforms are widely utilized to convey emotions, share opinions, and express preferences through various means such as artworks, multimedia content, and blogs. These individual-specific traits have a wide range of applications such as personalized recommender systems, human behavior analysis, human-computer interaction, robotics, and biometric security. Aesthetic biometric systems utilize users’ unique preferences towards various subjective forms such as images, music, and textual content. This study introduces a novel deep learning-based multi-modal aesthetic system, with a primary contribution to the development of an attention-based fusion method for person identification. The proposed identification system leverages a deep pre-trained model for high-level feature extraction from visual and auditory modalities. The paper introduces a novel fusion architecture named attention-based residual fusion network (ARF-Net) to incorporate two heterogeneous aesthetic modalities. The proposed system is validated on two proprietary aesthetic datasets outperforming the existing state-of-the-art aesthetic biometric systems for person identification. The proposed architecture stands out for its efficiency, showcasing a lightweight architecture with minimal parameters, ensuring optimal performance across multiple aesthetic modalities.
dc.identifier.citationIffath, F. (2024). AestheticID: Human Identification Using Audio-Visual Preferences (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/120060
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity 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.subjectBiometrics
dc.subjectAesthetics
dc.subjectPerson Identification
dc.subjectInformation Fusion
dc.subjectDeep Learning
dc.subject.classificationComputer Science
dc.titleAestheticID: Human Identification Using Audio-Visual Preferences
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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