Device-free localization (DFL) localizes the target by employing the variation of received signal strength (RSS) due to the presence of the target which wears no device. The existing fingerprint-based DFL (FDFL) method simply compares the online RSS variations with the radio map and does not care about how RSS variation is caused. However, we have found that when some links are shadowed by the target, even in indoor environments, the RSS of the links will experience large attenuation. Hence, in this paper, we propose to leverage shadowed links to enhance the performance of FDFL method. Specifically, we first detect the shadowed links in the monitored region, where the detection takes both the RSS variations and fade levels of the links into consideration. Afterwards, we reformulate the fingerprint matching by reducing the search space into the shadow region for improving the localization accuracy. Moreover, we propose a geometrical localization (GL) method if shadow regions of the detected shadowed links intersect. The experimental results show that the performance of the proposed method has been improved significantly compared to the traditional FDFL method.