Performance Comparison of Principal Component Analysis-Based Face Recognition in Color Space

Abstract

Light reflected from an object is multi-spectral, and human beings recognize the object by perceiving color spectrum of the visible light (Wyszecki & Stiles, 2000). However, most of face recognition algorithms have used only luminance information (Bartlett et al., 2002; Belhumeur et al., 1997; Etemad & Chellappa, 1997; Liu & Wechsler, 2000; Turk & Pentland, 1991a, 1991b; Wiskott et al., 1997; Yang, 2002). Many face recognition algorithms convert color input images to grayscale images by discarding their color information. Only a limited number of face recognition methods made use of color information. Torres et al. proposed a global eigen scheme to make use of color components as additional channels (Torres et al., 1999). They reported color information could potentially improve performance of face recognition. Rajapakse et al. proposed a non-negative matrix factorization method to recognize color face images and showed that the color image recognition method is better than grayscale image recognition approaches (Rajapakse et al., 2004). Yang et al. presented the complex eigenface method that combines saturation and intensity components in the form of a complex number (Yang et al., 2006). This work shows that the multi-variable principal component analysis (PCA) method outperforms traditional grayscale eigenface methods. Jones III and Abbott showed that the optimal transformation of color space into monochrome form can improve the performance of face recognition (Jones III & Abbott, 2004), and Neagoe extended the optimal transformation to two-dimensional color space (Neagoe, 2006). Color images include more visual clues than grayscale images, and the above-mentioned work showed effectiveness of color information for face recognition. However, there is lack of analysis and evaluation regarding the recognition performance in various color spaces. A large number of face recognition algorithms (Bartlett et al., 2002; Belhumeur et al., 1997; Etemad & Chellappa, 1997; Liu & Wechsler, 2000; Turk & Pentland, 1991a, 1991b; Wiskott et al., 1997; Yang, 2002) have been presented. This paper is an extended version of the paper (Yoo et al., 2007), in which analysis of the recognition rate in various color spaces with two different approaches in CMU PIE database (Sim et al., 2003; Zheng et al., 2005) and color FERET database (Phillips et al., 1998, Phillips et al., 2000) is supplemented. Note that PCA-based algorithms are employed since they are the most fundamental and prevalent approaches. Recognition performance is evaluated in various