This paper presents a new Multidirectional Binary Pattern (MBP) for face recognition. Different from most Local Binary Pattern (LBP) related approaches which cluster LBP occurrences from whole image or partitioned subimage patches and use single or concatenated histogram measurement for recognition, MBP is applied on a sparse set of shape-driven points. The new representation is designed for describing both global structure and local texture, and also significantly reduces the high imensionality of LBP histogram description. Composed of binary patterns from multiple directions, MBP is capable of extracting more discriminative features than LBP. The experiments on face recognition demonstrated the effectiveness of the proposed algorithm against expression and lighting variations.