In previous studies on human face recognition, illumination pretreatment has been considered to be among the most crucial steps. We propose the illumination compensation algorithm two separated singular value decomposition (TSVD). TSVD consists of two parts, namely the division of high- and low-level images and singular value decomposition, which are implemented according to self-adapted illumination compensation to resolve the problems associated with strong variation of light and to improve face recognition performance. The mean color values of the three color channels R, G, and B are used as the thresholds, and two subimages of two types of light levels are then input with the division of the maximal mean and minimal mean, which are incorporated with light templates at various horizontal levels. The dynamic compensation coefficient is proportionately adjusted to reconstruct the subimages. Finally, two subimages are integrated to achieve illumination compensation. In addition, we combined TSVD and the projection color space (PCS) to design a new method for converting the color space called the two-level PCS. Experimental results demonstrated the efficiency of our proposed method. The proposed method not only makes the skin color of facial images appear softer but also substantially improves the accuracy of face recognition, even in facial images that were taken under conditions of lateral light or exhibit variations in posture.