A moment gait energy image (MGEI) based gait recognition algorithm is presented.Gait period is estimated from swing distances

and key moments of a gait cycle are specified.The mean of all the silhouette images at a key moment is called the mean moment image.The MGEI at each key moment is calculated from all the mean moment images in gait period.It improves silhouette's quality by gait probability distribution at each key moment

and provides more motion features than the basic gait energy image (GEI).Then a method of gait feature extraction is proposed based on MGEI.The deviations of silhouette images from MGEI at every key moment are cumulated into an image

which is utilized together with GEI to represent a subject.The nearest neighbor classifier is adopted to recognize subjects.The proposed algorithm is evaluated on USF dataset

and the performance is compared with the baseline algorithm and two other new algorithms.Experimental result shows that this algorithm achieves higher overall recognition rate then the other algorithms.