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Face recognition breakthrough

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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

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Abstract

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

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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
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Index Terms

Face recognition breakthrough
1. Computing methodologies
  1. Machine learning

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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 ...

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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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2009

Published in CACM Volume 52, Issue 8

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Cited By

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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
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Recommendations

Age-Invariant Face Recognition

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

Face recognition under varying illumination using gradientfaces

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