Person Identification
Person identification encompasses a broad range of techniques aimed at recognizing and classifying individuals from various data sources, including images, videos, audio, and even physiological signals. Current research emphasizes robust methods for handling noisy or incomplete data, focusing on deep learning architectures like convolutional neural networks, recurrent neural networks, and graph neural networks, as well as optimization algorithms such as Bayesian optimization and projected gradient descent. These advancements have significant implications for applications such as security, healthcare, and human-computer interaction, improving accuracy and efficiency in tasks ranging from biometric authentication to personalized medicine.
Papers
Deep Learning methodology for the identification of wood species using high-resolution macroscopic images
David Herrera-Poyatos, Andrés Herrera-Poyatos, Rosana Montes, Paloma de Palacios, Luis G. Esteban, Alberto García Iruela, Francisco García Fernández, Francisco Herrera
Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains
Evan M. Williams, Peter Carragher, Kathleen M. Carley
Identification of Physical Properties in Acoustic Tubes Using Physics-Informed Neural Networks
Kazuya Yokota, Masataka Ogura, Masajiro Abe
Low-resource speech recognition and dialect identification of Irish in a multi-task framework
Liam Lonergan, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, Ailbhe Ní Chasaide
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach
Xuyao Feng, Anthony Hunter