While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art. © 2014 Chinese Association of Automation.