Computer Vision and Machine Learning for Biometric Systems

Dear Colleagues,

Biometric systems have seen rapid growth and technological advancements in recent years, largely driven by the progress being made in computer vision, machine learning, and image processing. Techniques such as deep learning, convolutional neural networks, and advanced image analysis methods are being leveraged to improve the accuracy, robustness, and scalability of biometric recognition systems. This Special Issue aims to gather cutting-edge research on how these interdisciplinary fields can be utilized to develop, enhance, and evaluate various biometric modalities, including face, iris, fingerprint, voice, and gait recognition.

Contributions are sought on all facets of the development of biometric systems, from novel algorithmic innovations and optimized model architectures to considerations needed for real-world deployment, such as their performance in challenging environments, security vulnerabilities, and ethical concerns. Submissions that explore multimodal fusion, domain adaptation for diverse user populations, and integration with edge or IoT devices are particularly welcome. We also encourage studies examining the broader ecosystem of biometric technologies, including privacy-preserving techniques and regulatory frameworks. By bringing together researchers, practitioners, and industry experts, this Special Issue aims to advance the field of biometric systems and highlight the future directions in which computer vision, machine learning, and image processing can shape the next generation of reliable and secure authentication methods.

Dr. Lehel Denes-Fazakas
Dr. László Szilágyi
Prof. Dr. Eva H. Dulf
Guest Editors

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