Color face recognition by PCA-like approach

• Color two-dimensional principal component analysis (C2DPCA) is an improvement from 2DPCA with color value model for color face recognition.
• Color principal component analysis (CPCA) is an improvement from PCA with color images matrix-representation model for color recognition.
• We use the C2DPCA and CPCA for face recognition and achieve the satisfactory results.

In this paper, a novel technique aimed to make full use of the color cues is proposed to improve the accuracy of color face recognition based on principal component analysis. Principal component analysis (PCA) has been an important method in the field of face recognition since the very early stage. Later, two-dimensional principal component analysis (2DPCA) was developed to improve the accuracy of PCA. However, the color information is omitted since the images need to be transformed into a greyscale version before applying both of the two methods. In order to exploit the color information to recognize faces, we propose a novel technique which utilizes color images matrix-representation model based on the framework of PCA for color face recognition. Furthermore, a color 2DPCA (C2DPCA) method is devised to combine the spatial and color information for color face recognition. Experiment results show that our proposed methods can achieve higher accuracy than regular PCA methods.