Feature extraction and learning approaches for cancellable biometrics: A survey

Abstract

Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

“…However, biases exist within data sources like SIDER, which may skew towards common side effects [ 50 ], and limitations in PubChem exclude information on biologic drugs, urging integration with databases capturing biologic complexities [ 51 ]. Feature engineering techniques, like fingerprint generation algorithms and text-mining, aid in translating raw data into interpretable formats [ 52 ], while network-based approaches offer promise in modeling complex relationships between chemical structures, biological targets, and side effects [ 53 ]. Despite the potential of emerging data sources such as electronic health records and genomics data for personalized prediction, challenges like data standardization and interoperability persist [ 54 ], highlighting the need for standardized efforts and common ontologies to facilitate comprehensive dataset creation for machine learning models in side effect prediction.…”