Machine Learning Techniques on Biometrics and IoT Applications

Dear Colleagues,

At present, the use of machine learning techniques can be found in many different applications running on diverse software and hardware platforms, for distinct purposes. Even though machine learning is a mature research field, it is still evolving in different active research directions, seeking to improve and to simplify many of our daily tasks and activities. The boom of deep learning has led to broader, increased attention in the field. Although this sometimes appears under the more generic name of artificial intelligence, or simply AI, in many cases, we are dealing with machine learning techniques.

We can find a strong connection between machine learning techniques and other evolved research fields such as biometric systems and the emerging Internet of Things (IoT) paradigm, where the data-driven approach is growing, and it is expected that we will witness the expansion of applications, services, and devices in the upcoming years.

For years, biometric systems have been used for authentication and identification of individuals or simply to acquire, process, and analyze user data and vital signs. This year, the COVID-19 pandemic situation created the urgent need for new applications—for example, student authentication in e-learning systems—where confirmation of identity is crucial for non-presence distance course assessment.

Additionally, the use of contactless biometric traits is more adequate to deal with pandemic situations caused by viruses but leads to additional challenges in deployment. In all these scenarios, the use of machine learning techniques plays a decisive role in the accuracy and robustness of these systems.

The IoT field has gained a lot of research and development interest in the past few years. Pervasive computing systems, also known as ubiquitous computing systems, resort to different processing nodes such as microprocessors and sensors that communicate over a network and are usually available on all-time basis. These nodes produce significant amounts of data that need to be processed and interpreted by some machine learning techniques, again under the generic name of AI. Edge computing scenarios are also of increased interest, in order to decrease the communication bandwidth but also because of privacy issues. Local processing of raw sensor information removing sensitive information, and creation of sufficient features for each application are crucial for large-scale deployment.

In summary, in this Special Issue, we aim to publish papers that address the use of machine learning techniques for biometrics systems and Internet of Things (IoT) applications, on different software and hardware platforms.

Prof. Dr. Artur Jorge Ferreira
Prof. Dr. André Ribeiro Lourenço
Guest Editors

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• machine learning
• biometrics
• biometric application
• internet of things
• data mining
• supervised learning
• unsupervised learning
• clustering