Guest Editors
Dr. Marco Piangerelli
Email: marco.piangerelli@unicam.it
Affiliation: Computer Science Division, University of Camerino, 62032 Camerino, Italy
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Research Interests: machine learning, computer vision, unsupervised learning, ViT
Summary
Face recognition has emerged as a pivotal technology in the realm of computer vision, with applications spanning security, healthcare, human-computer interaction, smart cities, and social media. The rapid evolution of deep learning, edge computing, and multimodal data processing has significantly enhanced the performance of face recognition systems. However, challenges such as robustness under unconstrained environments, fairness, privacy, and adversarial threats remain critical concerns.
This Special Issue aims to bring together cutting-edge research and practical advancements in face recognition. We invite contributions that address theoretical foundations, algorithmic innovations, system-level implementations, and real-world applications. We particularly encourage interdisciplinary contributions that advance face recognition through synergies with edge computing, AI hardware architectures, and novel sensing modalities. Such integrations are essential for addressing scalability, energy efficiency, and deployment constraints in real-world environments, thereby fulfilling the journal's mission to unify computing and engineering disciplines.
Topics of Interest (include but are not limited to):
· Deep learning architectures for face recognition
· Lightweight and real-time face recognition on edge devices
· 3D face modeling and recognition
· Face recognition under occlusion, low resolution, and varying lighting
· Cross-domain and cross-modal face recognition
· Privacy-preserving and federated learning approaches
· Face anti-spoofing and deepfake detection
· Robustness and generalization under unconstrained environments
· Hardware-aware and edge-optimized face recognition algorithms
· Hardware acceleration and neuromorphic computing for face recognition
Keywords
face detection, privacy-preserving AI, adversarial robustness, smart surveillance, edge computing, facial landmark localization
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