This paper presents a supervised classification task on gait biometric of siblings’ data sets. This task, which we refer to as matched-pair classification, evaluates the within pair differences interms of the data set via jackknifing. A misclassification rate (MCR), which measures the percentage of misclassification of one sib compared to the other, gives an estimate on the potential uniqueness of gait for a person, particularly in twins. By this approach, the MCR values are mostly in the range of 90% for a data set of twins and in the range of 80% for a data set of non-twin siblings. When compared to the standard Leave-One-Out (LOO) classification, the MCR values of the proposed approach are higherthan the LOO classification, which may suggest its potential use in machine learning with regard to biometric-based systems.