A hand vein recognition method using local Gabor ordinal measure (OM) is presented. Gabor OM uses eight encoding masks to extract four types of features, which are derived from the magnitude, phase, real, and imaginary components of vein image after Gabor filtering, respectively, and then concatenates these feature histograms. Block-based pattern matching introduced with a Fisher linear discriminant adopts the “divide and conquer” strategy to alleviate the effect of noise and to further enhance the discriminative power of the feature descriptor. The proposed method is evaluated on our hand vein image database and HK PolyU database. The results with an error equation rate of 0.53% and 0.06% on the two databases, respectively, demonstrate the good performance of our approach.