Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary

Guojun LIN; Mei XIE; Ling MAO

doi:10.1587/transinf.E96.D.2290

Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary

Guojun LIN, Mei XIE, Ling MAO

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For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.10 pp.2290-2293

Publication Date: 2013/10/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.2290

Type of Manuscript: LETTER

Category: Image Recognition, Computer Vision

Authors

Guojun LIN
  University of Electronic Science and Technology of China
Mei XIE
  University of Electronic Science and Technology of China
Ling MAO
  University of Electronic Science and Technology of China

Keyword

face recognition, sparse representation, collaborative representation, single training image

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Guojun LIN, Mei XIE, Ling MAO, "Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 10, pp. 2290-2293, October 2013, doi: 10.1587/transinf.E96.D.2290.
Abstract: For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2290/_p

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@ARTICLE{e96-d_10_2290,
author={Guojun LIN, Mei XIE, Ling MAO, },
journal={IEICE TRANSACTIONS on Information},
title={Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary},
year={2013},
volume={E96-D},
number={10},
pages={2290-2293},
abstract={For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.},
keywords={},
doi={10.1587/transinf.E96.D.2290},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary
T2 - IEICE TRANSACTIONS on Information
SP - 2290
EP - 2293
AU - Guojun LIN
AU - Mei XIE
AU - Ling MAO
PY - 2013
DO - 10.1587/transinf.E96.D.2290
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2013
AB - For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.
ER -