Lipreading by Locality Discriminant Graph

The major problem in building a good lipreading system is to extract effective visual features from the enormous quantity of video sequences data. For appearance-based feature analysis in lipreading, classical methods, e.g. DCT, PCA and LDA, are usually applied to dimensionality reduction. We present a new pattern classification algorithm, called locality discriminant graph (LDG), and develop a novel lipreading framework to successfully apply LDG to the problem. LDG takes the advantages of both manifold learning and Fisher criteria to seek the linear embedding which preserves the local neighborhood affinity within same class while discriminating the neighborhood among different classes. The LDG embedding is computed in closed-form and tuned by the only open parameter of k-NN number. Experiments on AVICAR corpus provide evidence that the graph-based pattern classification methods can outperform classical ones for lipreading.