Thomas Swearingen

TL;DR: The initial results show ATM can beat human-generated solutions for 30% of the datasets, and can do so in 1/100th of the time, and the usefulness of ATM is demonstrated.

TL;DR: This work analyzes the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another person's facial appearance, and studies the impact of makeup-induced face spoofing on automated face recognition.

TL;DR: The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service as discussed by the authors.

TL;DR: There are several fundamental issues that are yet to be satisfactorily resolved in biometric technology and this article will discuss some of these issues and enumerate some of the exciting challenges in this field.

TL;DR: This paper surveys research to date on the problem of using iris images acquired after death for automated human recognition, and provides a medically informed commentary on post-mortem iris, analyze the reasons for recognition failures, and identify key directions for future research.