Lip features selection with application to person authentication

Authentication system solely based on visual lip information is of advantage since the uttering characteristics/manner is unique to individual. This paper presents the results of studying for the most appropriate features extracted from the lip motion for person authentication application. The geometric-based, shape-based and inner lip features are derived from a 14-point active shape model (ASM) lip model. The dynamic features reflecting the differential changes of the feature parameters are also considered in the experiment. A database consists of 40 speakers and each of their utterance last for 3 seconds. Various combinations of features obtained by analyzing this database are fed into a hidden Markov model (HMM) classifier for processing. It is observed that the best result has been obtained when both shape-based features and inner lip features are used.