Comparative Study on Color Components for PCA-Based Face Recognition

Dongzhu Yin; Yoshihiro Sugaya; Shinichiro Omachi; Hirotomo Aso

doi:10.11371/iieej.40.671

Comparative Study on Color Components for PCA-Based Face Recognition

Dongzhu Yin, Yoshihiro Sugaya, Shinichiro Omachi, Hirotomo Aso

ENG - Communications Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Using color information can significantly improve the face recognition rate instead of using the grayscale luminance image. However, there are few works that try to compare the color space models on face recognition. In this paper, we investigate thirty different color space models on face recognition using the classical principal component analysis (PCA). Through the extensive experiments we find that after successfully diminishing the influence of the illumination the recognition accuracy can be improved by 4.6~5.5 percent points.

Original language	English
Pages (from-to)	671-678
Number of pages	8
Journal	Journal of the Institute of Image Electronics Engineers of Japan
Volume	40
Issue number	4
DOIs	https://doi.org/10.11371/iieej.40.671
Publication status	Published - 2011 Jan

Keywords

color space
face recognition
principal component analysis

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10.11371/iieej.40.671

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abstract = "Using color information can significantly improve the face recognition rate instead of using the grayscale luminance image. However, there are few works that try to compare the color space models on face recognition. In this paper, we investigate thirty different color space models on face recognition using the classical principal component analysis (PCA). Through the extensive experiments we find that after successfully diminishing the influence of the illumination the recognition accuracy can be improved by 4.6~5.5 percent points.",

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Comparative Study on Color Components for PCA-Based Face Recognition

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