Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Face recognition has received significant attention because of its numerous applications in
access control, law enforcement, security, surveillance, Internet communication and …

EXECUTIVE SUMMARY OVERVIEW This is the third in a series of reports on ongoing face
recognition vendor tests (FRVT) executed by the National Institute of Standards and …

Racial bias is an important issue in biometric, but has not been thoroughly studied in deep
face recognition. In this paper, we first contribute a dedicated dataset called Racial Faces in …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

Although deep face recognition has achieved impressive progress in recent years,
controversy has arisen regarding discrimination based on skin tone, questioning their …

Inspired by the primate visual system, deep convolutional neural networks (DCNNs) have
made impressive progress on the complex problem of recognizing faces across variations of …

Facial image comparison practitioners compare images of unfamiliar faces and decide
whether or not they show the same person. Given the importance of these decisions for …

Face image quality can be defined as a measure of the utility of a face image to automatic
face recognition. In this paper, we propose (and compare) two methods for learning face …

Face recognition is thought to rely on representations that encode holistic properties.
Paradoxically, professional forensic examiners who identify unfamiliar faces by comparing …