This paper presents a approach for gait recognition based on binarized silhouette of a motion object which is represented by distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. First, gait cycle estimation is performed based on normalized correlation on the distance vectors. Gait patterns are then extracted by using distace vectors for each projection independently. Then gait patterns are normalized according to dimensions of bounding box and gait cycle. Second, PCA based eigenspace transform is applied to gait patterns and Euclidean distances based supervised pattern classification is finaly performed in the lower-dimensional eigenspace for human identification. Experimental results on four databases (CMU MoBo, SOTON, USF, NLPR) show that the proposed approach achieves highly compettive performance with respect to the published gait recognition approaches.