IEICE TRANSACTIONS on Information
Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary
Summary :
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
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2290/_p
Copy
@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},}
Copy
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 -
English
