Improving the face recognition grand challenge baseline performance using color configurations across color spaces

Peichung Shih; Chengjun Liu

doi:10.1109/ICIP.2006.312668

Improving the face recognition grand challenge baseline performance using color configurations across color spaces

Peichung Shih, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

69 Scopus citations

Abstract

This paper presents a method that applies color information to improve face recognition performance of the Face Recognition Grand Challenge (FRGC) baseline algorithm, also known as the Biometric Experimentation Environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YC_bC_r color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver .0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.

Original language	English (US)
Title of host publication	2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages	1001-1004
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2006.312668
State	Published - 2006
Externally published	Yes
Event	2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States Duration: Oct 8 2006 → Oct 11 2006

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Other

Other	2006 IEEE International Conference on Image Processing, ICIP 2006
Country/Territory	United States
City	Atlanta, GA
Period	10/8/06 → 10/11/06

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2006.312668

Cite this

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title = "Improving the face recognition grand challenge baseline performance using color configurations across color spaces",

abstract = "This paper presents a method that applies color information to improve face recognition performance of the Face Recognition Grand Challenge (FRGC) baseline algorithm, also known as the Biometric Experimentation Environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YCbCr color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver .0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.",

author = "Peichung Shih and Chengjun Liu",

year = "2006",

doi = "10.1109/ICIP.2006.312668",

language = "English (US)",

isbn = "1424404819",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "1001--1004",

booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",

}

Shih, P & Liu, C 2006, Improving the face recognition grand challenge baseline performance using color configurations across color spaces. in 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings., 4106701, Proceedings - International Conference on Image Processing, ICIP, pp. 1001-1004, 2006 IEEE International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 10/8/06. https://doi.org/10.1109/ICIP.2006.312668

Improving the face recognition grand challenge baseline performance using color configurations across color spaces. / Shih, Peichung; Liu, Chengjun.
2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings. 2006. p. 1001-1004 4106701 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Shih, Peichung

AU - Liu, Chengjun

PY - 2006

Y1 - 2006

N2 - This paper presents a method that applies color information to improve face recognition performance of the Face Recognition Grand Challenge (FRGC) baseline algorithm, also known as the Biometric Experimentation Environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YCbCr color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver .0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.

AB - This paper presents a method that applies color information to improve face recognition performance of the Face Recognition Grand Challenge (FRGC) baseline algorithm, also known as the Biometric Experimentation Environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YCbCr color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver .0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.

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BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings

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Y2 - 8 October 2006 through 11 October 2006

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Improving the face recognition grand challenge baseline performance using color configurations across color spaces

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