Video-based person authetication with random passwords

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 visual features are obtained by locating a region-of-interest and extracting geometric and textural features from the region. The extracted feature vectors are then classified by 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. Our results have demonstrated that random passwords provide useful information for speaker verification. Also, by using the proposed method, we observe a significant improvement in verification rate.