This paper addresses two critical but rarely concerned issues in 2D face recognition: wider-range tolerance to pose variation and misalignment. We propose a new Textural Hausdorff Distance (THD), which is a compound measurement integrating both spatial and textural features. The THD is applied to a Significant Jet Point (SJP) representation of face images, where a varied number of shape-driven SJPs are detected automatically from low-level edge map with rich information content. The comparative experiments conducted on publicly available FERET and AR face databases demonstrated that the proposed approach has a considerably wider range of tolerance against both in-depth head rotation and face misalignment.