Abstract
The proposed system aims at providing a novel framework of speaker authentication using lip-motion data. The system is divided into three steps: feature extraction and modeling, model synthesis, and probabilistic model matching. First, the lip images are obtained by locating a region-of-interest and then the local textural features are extracted by Local Binary Pattern (LBP) operator. The next step is using K-means clustering to obtain a reduced number of observation vectors. These observation vectors are fed into a set of Hidden Markov Models (HMMs) classifiers to capture the temporal characteristics of the features. The main contribution of this paper lies in the introduction of using random passwords in performing speaker verification. Experimental results demonstrate that random passwords provide useful information for speaker verification. Also, by using the proposed method, we observe a significant improvement in verification rate.