To date, almost all experimental evaluations of machine learning-based recognition algorithms
in computer vision have taken the form of “closed set” recognition, whereby all testing …

Real-world tasks in computer vision often touch upon open set recognition: multi-class
recognition with incomplete knowledge of the world and many unknown inputs. Recent work on …

… This paper extends the recent statistical learning work of Scheirer et al. [43,42], which is
limited to closed set problems. Our extension directly models the probability of inclusion for open …

This paper is a security analysis of leading privacy enhanced technologies (PETs) for
biometrics including biometric fuzzy vaults (BFV) and biometric encryption (BE). The lack of …

It is often desirable to be able to recognize when inputs to a recognition function learned in
a supervised manner correspond to classes unseen at training time. With this ability, new …

Existing enhancement methods are empirically expected to help the high-level end computer
vision task: however, that is observed to not always be the case in practice. We focus on …

In this paper, we define meta-recognition, a performance prediction method for recognition
algorithms, and examine the theoretical basis for its postrecognition score analysis form …

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots,
and distributed networks like Tor has drastically complicated the task of identifying users …

Recent work has shown that visual attributes are a powerful approach for applications such
as recognition, image description and retrieval. However, fusing multiple attribute scores - as …

This paper reviews the biometric dilemma, the pending threat that may limit the long-term
value of biometrics in security applications. Unlike passwords, if a biometric database is ever …