AestheticID: Human Identification Using Audio-Visual Preferences
Date
2024-11-14
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Over 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.
Description
Keywords
Biometrics, Aesthetics, Person Identification, Information Fusion, Deep Learning
Citation
Iffath, F. (2024). AestheticID: Human Identification Using Audio-Visual Preferences (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.