department

Free access

Face recognition breakthrough

Author:

Kirk L. KroekerAuthors Info & Claims

Communications of the ACM, Volume 52, Issue 8

Pages 18 - 19

https://doi.org/10.1145/1536616.1536623

Published: 01 August 2009 Publication History

All formats PDF

Abstract

By using sparse representation and compressed sensing, researchers have been able to demonstrate significant improvements in accuracy over traditional face-recognition techniques.

Cited By

View all

Ahmed SFrikha MHussein TRahebi J(2021)[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep LearningBioMed Research International10.1155/2021/66215402021:1Online publication date: 10-Mar-2021
https://doi.org/10.1155/2021/6621540
Liu ZJiang DLi YCao YWang MXu Y(2019)Automatic Face Recognition Based on Sparse Representation and Extended Transfer LearningIEEE Access10.1109/ACCESS.2018.28832887(2387-2395)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2883288
Yang JLiu Y(2019)Undersampled face recognition based on virtual samples and representation classificationNeural Computing and Applications10.1007/s00521-017-3204-431:7(2447-2453)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00521-017-3204-4
Show More Cited By

Index Terms

Face recognition breakthrough
1. Computing methodologies
  1. Machine learning

Recommendations

Age-Invariant Face Recognition

One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that ...
Face recognition: face in video, age invariance, and facial marks
Face recognition under varying illumination using gradientfaces

In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and ...

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 52, Issue 8

A Blind Person's Interaction with Technology

August 2009

132 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1536616

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2009

Published in CACM Volume 52, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Department
Popular
Un-reviewed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
3,191
Total Downloads

Downloads (Last 12 months)66
Downloads (Last 6 weeks)4

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ahmed SFrikha MHussein TRahebi J(2021)[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep LearningBioMed Research International10.1155/2021/66215402021:1Online publication date: 10-Mar-2021
https://doi.org/10.1155/2021/6621540
Liu ZJiang DLi YCao YWang MXu Y(2019)Automatic Face Recognition Based on Sparse Representation and Extended Transfer LearningIEEE Access10.1109/ACCESS.2018.28832887(2387-2395)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2883288
Yang JLiu Y(2019)Undersampled face recognition based on virtual samples and representation classificationNeural Computing and Applications10.1007/s00521-017-3204-431:7(2447-2453)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00521-017-3204-4
Wang QZhang YXiao LLi Y(2019)Extension of Sample Dimension and Sparse Representation Based Classification of Low-Dimension DataGenetic and Evolutionary Computing10.1007/978-981-13-5841-8_66(633-640)Online publication date: 12-May-2019
https://doi.org/10.1007/978-981-13-5841-8_66
Andrus CGaynor M(2018)Successful HIT Requires Inter-Team CommunicationJournal of Cases on Information Technology10.4018/jcit.201310010115:4(1-6)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/jcit.2013100101
Wang XChen Y(2017)Differentiated representation and applications to face recognitionOptik10.1016/j.ijleo.2017.04.055140(216-222)Online publication date: Jul-2017
https://doi.org/10.1016/j.ijleo.2017.04.055
Zhang HLi FLiu PChen YRen DWang K(2017)How can a sparse representation be made applicable for very low-dimensional data?Expert Systems with Applications: An International Journal10.1016/j.eswa.2017.01.03977:C(66-70)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.01.039
Zhu QZhang DSun HLi Z(2015)Combining L1-norm and L2-norm based sparse representations for face recognitionOptik10.1016/j.ijleo.2015.02.020126:7-8(719-724)Online publication date: Apr-2015
https://doi.org/10.1016/j.ijleo.2015.02.020
Ganesan RMurarka YSarkar SFrey KShyamasundar RShastri LJanakiram DPadmanabhuni S(2013)Empirical study of performance benefits of hardware assisted virtualizationProceedings of the 6th ACM India Computing Convention10.1145/2522548.2522598(1-8)Online publication date: 22-Aug-2013
https://dl.acm.org/doi/10.1145/2522548.2522598
Wang XChen ZLin Z(2013)Class-Nearest Neighbor Classifier for Face RecognitionProceedings of the 2013 International Conference on Computer Sciences and Applications10.1109/CSA.2013.82(325-328)Online publication date: 14-Dec-2013
https://dl.acm.org/doi/10.1109/CSA.2013.82
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Age-Invariant Face Recognition

Face recognition: face in video, age invariance, and facial marks

Face recognition under varying illumination using gradientfaces

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations