Visual Speaker Authentication Research Based On Dynamic Lip Details

Lip biometric is easy to collect,hard to forge and friendly to user and environment.The study of the discriminative power of lip has aroused the interest of many researchers in recent years.This paper presents a new visual speaker authentication scheme which can extract the most representative details of a speaker's lip feature.For each speaker,the entire utterance pronouncing a specific prompt text is divided into several word-level segments and a mute segment.Three kinds of lip feature details are investigated including:i)lip movements in each word segment;ii)lip movements in each word transition;and iii)lip appearance in the mute segment.A HMM with model adaptation is adopted to depict each dynamic details and a linear SVM is used to differentiate the static details.A confident measure is introduced to evaluate the discriminative ability of each details between the speaker and others.Finally,an ensemble learning structure is proposed which focuses on the discriminative details and provides a reliable authentication result.Experimental results have demonstrated that our scheme achieves better performance compared with some traditional approaches investigated.In the experiment on standard lip database,the authentication result of the proposed scheme achieved 1.26%on HTER evaluation metric.