Discriminating Color Faces For Recognition

Abstract

Color provides useful and important information for object detection, tracking and recognition, image (or video) segmentation, indexing and retrieval, etc. [1-15]. Color constancy algorithms [13, 14] and color histogram techniques [5, 10-12], for example, provide efficient tools for indexing in a large image database or for object recognition under varying lighting conditions. Different color spaces (or color models) possess different characteristics and have been applied for different visual tasks. For instance, the HSV color space and the YCbCr color space were demonstrated effective for face detection [2, 3], and the modified L*u*v* color space was chosen for image segmentation [7]. Recently, a selection and fusion scheme of multiple color models was investigated and applied for feature detection in images [15]. Although color has been demonstrated helpful for face detection and tracking, some past research suggests that color appears to confer no significant face recognition advantage beyond the luminance information [16]. Recent research efforts, however, reveal that color may provide useful information for face recognition. The experimental results in [17] show that the principle component analysis (PCA) method [35] using color information can improve the recognition rate compared to the same method using only luminance information. The results in [18] further reveal that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Other research findings also demonstrate the effectiveness of color for face recognition [19-22, 38]. If color does help face recognition, then a question arises: how should we represent color images for the recognition purpose? One common practice is to convert color images in the RGB color space into a grayscale image by averaging the three color component images before applying a face recognition algorithm for recognition. However, there are neither theoretical nor experimental justifications for supporting that such a grayscale image is a good representation of the color image for the recognition purpose. Other research effort is to choose an existing color space or a color component configuration for achieving good recognition performance with respect to a specific recognition method. For instance, Rajapakse et al. [19] used the RGB color space and nonnegative matrix factorization (NMF) method for face recognition. Torres et al. [17] suggested using the YUV color space or the configuration of S and V components from the HSV color space together with PCA for O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg