AI-Driven Pattern Recognition for Next-Generation Biometrics
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 26 February 2027 | Viewed by 155
Special Issue Editors
Interests: pattern recognition; computational intelligence; classification techniques, machine learning and deep learning; evolutionary computation, handwriting analysis and recognition; neurodegenerative diseases
Interests: artificial intelligence; machine learning; pattern recognition; deep learning; handwriting analysis; neurodegenerative diseases; specific learning disorders; evolutionary algorithms
Interests: artificial intelligence; machine learning; deep learning; pattern recognition; handwriting analysis; neurodegenerative diseases; specific learning disorders; evolutionary algorithms; agentic AI; optimisation
Special Issue Information
Dear Colleagues,
The convergence of artificial intelligence (AI) and biometrics is revolutionizing pattern recognition, moving beyond traditional methods to create intelligent, adaptive and secure identification systems. The proliferation of biometric data from IoT devices and mobile platforms presents both a challenge in terms of Big Data management and a tremendous opportunity for innovation. In this context, AI, particularly through advanced machine learning, deep learning and cognitive computing models, has emerged as the foundational technology for next-generation biometrics. These data-driven approaches autonomously learn hierarchical and discriminative features directly from raw data, dramatically improving accuracy in recognizing both physiological (e.g., face, iris, fingerprint, vein) and behavioral (e.g., gait, voice, keystroke dynamics) traits. This now extends to include nuanced behavioral patterns such as handwriting dynamics and full-body movement analysis, which offer rich, continuous authentication signals. More importantly, AI empowers systems to adapt dynamically, detect sophisticated presentation attacks (spoofing) and fuse information from multiple biometric sources for highly reliable authentication.
The importance of this AI-driven transformation cannot be overstated. It is pivotal for advancing global security frontiers, enabling seamless and natural human–machine interaction in smart environments and personalizing services in critical sectors such as finance, healthcare and smart city infrastructure. By pushing the boundaries of what is possible in pattern recognition, including the analysis of complex spatio-temporal patterns in handwriting and kinesthetic body movements, this research area is not merely an academic pursuit but a crucial enabler for a more secure, efficient and interconnected future.
This Special Issue, "AI-Driven Pattern Recognition for Next-Generation Biometrics", aims to collate high-quality, original research that showcases the transformative impact of AI and cognitive computing on the future of biometrics. We seek to explore innovative algorithms, architectures and applications that address the core challenges of modern biometric systems, including scalability, privacy and real-time performance in complex environments.
We invite contributions on themes including, but not limited to:
- Deep and Transfer Learning for Biometric Modalities
- Explainable AI for Robust and Transparent Biometrics
- Neuromorphic Computing for Embedded Biometric Systems
- Multi-Modal Biometric Fusion using Cross-Media Analysis
- AI-Powered Biometric Security and Spoof Detection
- Federated Learning and Privacy-Preserving Biometric Authentication
- Biometrics in IoT and Smart Environments
- Digital Twin Technology for Biometric System Simulation
- AI for Behavioral Biometrics: Handwriting, Gait and Full-Body Movement Analysis
We look forward to receiving your contributions.
Prof. Dr. Claudio De Stefano
Dr. Tiziana D’Alessandro
Dr. Emanuele Nardone
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- biometrics
- pattern recognition
- cognitive computing
- multi-modal fusion
- security and spoof detection
- IoT and smart environments
- privacy-preserving authentication
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