When doing the recognition of gaits, this paper uses the k-nearest neighbor algorithm to vote the model from k nearest neighbors. It is an easy way to classify the new sample into one class, because we only need to find the distance between the input sample and training samples. One problem occurs here is how to select k and the size of training set. Since the probability of the k-nearest neighbor assumption can be formulated, if we are given the probability of the occurrence of each model, is there any method we could use this information to find the best k.