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Face recognition is a challenging problem, especially when the face images are not strictly aligned (e.g., images can be captured from different viewpoints or the faces may not be accurately cropped by a human or automatic algorithm). In this correspondence, we investigate face recognition under the scenarios with potential spatial misalignments. First, we formulate an asymmetric similarity measure based on Spatially constrained Earth Mover's Distance (SEMD), for which the source image is partitioned into nonoverlapping local patches while the destination image is represented as a set of overlapping local patches at different positions. Assuming that faces are already roughly aligned according to the positions of their eyes, one patch in the source image can be matched only to one of its neighboring patches in the destination image under the spatial constraint of reasonably small misalignments. Because the similarity measure as defined by SEMD is asymmetric, we propose two schemes to combine the two similarity measures computed in both directions. Moreover, we adopt a distance-as-feature approach by treating the distances to the reference images as features in a kernel discriminant analysis (KDA) framework. Experiments on three benchmark face databases, namely the CMU PIE, FERET, and FRGC databases, demonstrate the effectiveness of the proposed SEMD.