Literature DB >> 20299708

FRVT 2006 and ICE 2006 large-scale experimental results.

P Jonathon Phillips1, W Todd Scruggs, Alice J O'Toole, Patrick J Flynn, Kevin W Bowyer, Cathy L Schott, Matthew Sharpe.   

Abstract

This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.

Entities:  

Mesh:

Year:  2010        PMID: 20299708     DOI: 10.1109/TPAMI.2009.59

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  9 in total

1.  Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security.

Authors:  Jan C Mazura; Krishna Juluru; Joseph J Chen; Tara A Morgan; Majnu John; Eliot L Siegel
Journal:  J Digit Imaging       Date:  2012-06       Impact factor: 4.056

2.  Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?

Authors:  Jacqueline G Cavazos; P Jonathon Phillips; Carlos D Castillo; Alice J O'Toole
Journal:  IEEE Trans Biom Behav Identity Sci       Date:  2020-09-29

Review 3.  Stable face representations.

Authors:  Rob Jenkins; A Mike Burton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-06-12       Impact factor: 6.237

4.  Face averages enhance user recognition for smartphone security.

Authors:  David J Robertson; Robin S S Kramer; A Mike Burton
Journal:  PLoS One       Date:  2015-03-25       Impact factor: 3.240

5.  A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis.

Authors:  Sheng-Hsun Hsieh; Yung-Hui Li; Wei Wang; Chung-Hao Tien
Journal:  Sensors (Basel)       Date:  2018-03-06       Impact factor: 3.576

6.  Human-Computer Interaction in Face Matching.

Authors:  Matthew C Fysh; Markus Bindemann
Journal:  Cogn Sci       Date:  2018-06-28

7.  Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms.

Authors:  P Jonathon Phillips; Amy N Yates; Ying Hu; Carina A Hahn; Eilidh Noyes; Kelsey Jackson; Jacqueline G Cavazos; Géraldine Jeckeln; Rajeev Ranjan; Swami Sankaranarayanan; Jun-Cheng Chen; Carlos D Castillo; Rama Chellappa; David White; Alice J O'Toole
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-29       Impact factor: 11.205

Review 8.  Biometrics: Going 3D.

Authors:  Gerasimos G Samatas; George A Papakostas
Journal:  Sensors (Basel)       Date:  2022-08-24       Impact factor: 3.847

9.  Learning context and the other-race effect: Strategies for improving face recognition.

Authors:  Jacqueline G Cavazos; Eilidh Noyes; Alice J O'Toole
Journal:  Vision Res       Date:  2018-04-06       Impact factor: 1.886

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.