Paper Title
Deep Metric Learning for User Identification: A Comprehensive Survey of Methods, Applications, and Challenges
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Registration ID: IJNRD_322665
Published ID: IJNRD2604551
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Abstract
One of the important problems in the field of security systems is reliably verifying the identity of a user or a person. Conventional biometric systems have large intra-class variation and inter-class similarity. Deep Metric Learning (DML) is a suitable method for addressing this problem due to its capability to learn a discriminative embedding space where similar identities are closer to each other. This paper tries to make an exhaustive review of recent advances, central techniques, and applications of DML in user identification. We trace DML's basic contribution to the classic area of face recognition and stretch it to innovative behavioral biometrics- such as identifying users by motion patterns in eXtended Reality (XR) and web browsing histories. We also describe resilient multimodal systems that fuse together such data as facial features plus dynamic signatures. The paper wraps up basic loss functions (e.g., Contrastive, Triplet), advanced variants, network architectures, and some of the most important optimizations such as hard negative mining. Found thereby are problems that shall continue to challenge to extend system scalability and generalization for new users without any form of retraining, not speaking of the deep dimensions toward privacy and security risks with sensitive biometric data. We particularly underscore the crucial and largely neglected issue of demographic bias (e.g., by race and gender) where high overall accuracy can conceal substantial disparities in performance. Key future directions are finally discussed, wherein we place self-supervised learning at the center, anticipate an explosion of Transformer-based models for behavioral data, and highlight Federated Learning as an imperative privacy-preserving solution. This review is meant to guide researchers by synthesizing current trends towards the interdisciplinary need to build systems that shall be accurate, scalable, secure and ethically fair.
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How To Cite (APA)
Priya Thakkar, Dr.Dheerajkumar Singh, & Dr. Dinesh Prajapati (April-2026). Deep Metric Learning for User Identification: A Comprehensive Survey of Methods, Applications, and Challenges. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), f373-f382. https://ijnrd.org/papers/IJNRD2604551.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : f373-f382
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Paper Reg. ID: IJNRD_322665
Published Paper Id: IJNRD2604551
Research Area: Other area not in list
Author Type: Indian Author
Country: Anand, Gujarat, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604551.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604551
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