The methodology for finding the same individual in a net- work of cameras must deal with significant changes in ap- pearance caused by variations in illumination, viewing an- gle and a person’s pose. Re-identification requires solving two fundamental problems: (1) determining a distance mea- sure between features extracted from different cameras that copes with illumination changes (metric learning); and (2) ensuring that matched features refer to the same body part (correspondence). Most metric learning approaches focus on finding a robust distance measure between bounding box images, neglecting the alignment aspects. In this paper, we propose to learn appearance measures for patches that are combined using a spring model for addressing the cor- respondence problem. We validated our approach on the VIPeR, i-LIDS and CUHK01 datasets achieving new state of the art performance.