| Autor: |
Jihyeon Kim, Tiong-Sik Ng, Andrew Beng Jin Teoh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Informatics and Web Engineering, Vol 3, Iss 3, Pp 302-313 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2821-370X |
| DOI: |
10.33093/jiwe.2024.3.3.19 |
| Popis: |
In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model’s capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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