Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four I-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette. Second, eigenspace transformation based on Principal Component Analysis is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for recognition. A fusion task is finally executed to produce final decision. Experimental results on three databases show that the proposed method is an effective and efficient gait representation for human identification, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.